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<div class="section" id="recipes">
<h1>Recipes<a class="headerlink" href="#recipes" title="Permalink to this headline">¶</a></h1>
<p>This section provides a set of recipes that can be used to add additional functionality to inspyred.
These recipes are not a part of the core library, but they have proven to be useful in the past for
real-world programs. If they continue to be useful, they may be incorporated into inspyred in a
future version.</p>
<div class="section" id="lexicographic-ordering">
<h2>Lexicographic Ordering<a class="headerlink" href="#lexicographic-ordering" title="Permalink to this headline">¶</a></h2>
<p>In multiobjective optimization problems, alternatives to Pareto preference include linear weighting of the
objectives and prioritizing the objectives from most to least important. Both of these methods essentially
reduce the problem to a single objective optimization. Obviously, the weighting of the objectives would be
handled entirely in the evaluator for the problem, so no special recipe is needed. But the prioritizing
of the objectives, which is also known as lexicographic ordering, requires some additional effort.</p>
<p>The fitness values for two individuals, <em>x</em> and <em>y</em>, should be compared such that, if the first objective
for <em>x</em> is “better” (i.e., lower when minimizing or higher when maximizing) than the first objective for
<em>y</em>, then <em>x</em> is considered “better” than <em>y</em>. If they are equal in that objective, then the second
objective is considered in the same way. This process is repeated for all objectives.</p>
<p>The following recipe provides a class to deal with such comparisons that is intended to function much like
the <code class="docutils literal"><span class="pre">inspyred.ec.emo.Pareto</span></code> class.
[<a class="reference download internal" href="_downloads/lexicographic.py"><code class="xref download docutils literal"><span class="pre">download</span></code></a>]</p>
<div class="highlight-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">functools</span>
<span class="nd">@functools.total_ordering</span>
<span class="k">class</span> <span class="nc">Lexicographic</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">True</span><span class="p">):</span>
<span class="k">if</span> <span class="n">values</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">values</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">values</span>
<span class="k">try</span><span class="p">:</span>
<span class="nb">iter</span><span class="p">(</span><span class="n">maximize</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
<span class="n">maximize</span> <span class="o">=</span> <span class="p">[</span><span class="n">maximize</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">values</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">maximize</span> <span class="o">=</span> <span class="n">maximize</span>
<span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">iter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__lt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">maximize</span><span class="p">):</span>
<span class="k">if</span> <span class="n">m</span><span class="p">:</span>
<span class="k">if</span> <span class="n">v</span> <span class="o"><</span> <span class="n">o</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">True</span>
<span class="k">elif</span> <span class="n">v</span> <span class="o">></span> <span class="n">o</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">False</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="n">v</span> <span class="o">></span> <span class="n">o</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">True</span>
<span class="k">elif</span> <span class="n">v</span> <span class="o"><</span> <span class="n">o</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">False</span>
<span class="k">return</span> <span class="bp">False</span>
<span class="k">def</span> <span class="nf">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">values</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">maximize</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">maximize</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">my_evaluator</span><span class="p">(</span><span class="n">candidates</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">fitness</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">candidate</span> <span class="ow">in</span> <span class="n">candidates</span><span class="p">:</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">fitness</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Lexicographic</span><span class="p">([</span><span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">False</span><span class="p">))</span>
<span class="k">return</span> <span class="n">fitness</span>
<span class="k">def</span> <span class="nf">my_generator</span><span class="p">(</span><span class="n">random</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()]</span>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">'__main__'</span><span class="p">:</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">Lexicographic</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">Lexicographic</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">Lexicographic</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">Lexicographic</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">e</span> <span class="o">=</span> <span class="n">Lexicographic</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">Lexicographic</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">u</span> <span class="o">=</span> <span class="n">Lexicographic</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">v</span> <span class="o">=</span> <span class="n">Lexicographic</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">w</span> <span class="o">=</span> <span class="n">Lexicographic</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">Lexicographic</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">Lexicographic</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">Lexicographic</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="p">[</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">e</span><span class="p">,</span> <span class="n">f</span><span class="p">]:</span>
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="p">[</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">e</span><span class="p">,</span> <span class="n">f</span><span class="p">]:</span>
<span class="k">print</span><span class="p">(</span><span class="s">'</span><span class="si">%s</span><span class="s"> < </span><span class="si">%s</span><span class="s"> : </span><span class="si">%s</span><span class="s">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">p</span> <span class="o"><</span> <span class="n">q</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'----------------------------------------'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">]:</span>
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="p">[</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">]:</span>
<span class="k">print</span><span class="p">(</span><span class="s">'</span><span class="si">%s</span><span class="s"> < </span><span class="si">%s</span><span class="s"> : </span><span class="si">%s</span><span class="s">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">p</span> <span class="o"><</span> <span class="n">q</span><span class="p">))</span>
</pre></div>
</div>
</div>
<div class="section" id="constraint-selection">
<h2>Constraint Selection<a class="headerlink" href="#constraint-selection" title="Permalink to this headline">¶</a></h2>
<p>Optimization problems often have to deal with constraints and constraint violations. The following recipe
provides one example of how to handle such a thing with inspyred. Here, candidates represent ordered pairs
and their fitness is simply their distance from the origin. However, we provide a constraint that punishes
candidates that lie outside of the unit circle. Such a scenario should produce a candidate that lies on the
unit circle. Note also that <code class="docutils literal"><span class="pre">crowding_replacement</span></code> or some other fitness sharing or niching scheme could
be used to generate many such points on the circle.
[<a class="reference download internal" href="_downloads/constraint_selection.py"><code class="xref download docutils literal"><span class="pre">download</span></code></a>]</p>
<div class="highlight-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">random</span>
<span class="kn">from</span> <span class="nn">inspyred</span> <span class="kn">import</span> <span class="n">ec</span>
<span class="kn">from</span> <span class="nn">inspyred.ec</span> <span class="kn">import</span> <span class="n">variators</span>
<span class="kn">from</span> <span class="nn">inspyred.ec</span> <span class="kn">import</span> <span class="n">replacers</span>
<span class="kn">from</span> <span class="nn">inspyred.ec</span> <span class="kn">import</span> <span class="n">terminators</span>
<span class="kn">from</span> <span class="nn">inspyred.ec</span> <span class="kn">import</span> <span class="n">observers</span>
<span class="k">def</span> <span class="nf">my_constraint_function</span><span class="p">(</span><span class="n">candidate</span><span class="p">):</span>
<span class="sd">"""Return the number of constraints that candidate violates."""</span>
<span class="c"># In this case, we'll just say that the point has to lie </span>
<span class="c"># within a circle centered at (0, 0) of radius 1.</span>
<span class="k">if</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">my_generator</span><span class="p">(</span><span class="n">random</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="c"># Create pairs in the range [-2, 2].</span>
<span class="k">return</span> <span class="p">[</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">)]</span>
<span class="k">def</span> <span class="nf">my_evaluator</span><span class="p">(</span><span class="n">candidates</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="c"># The fitness will be how far the point is from</span>
<span class="c"># the origin. (We're maximizing, in this case.)</span>
<span class="c"># Note that the constraint heavily punishes individuals</span>
<span class="c"># who go beyond the unit circle. Therefore, these</span>
<span class="c"># two functions combined focus the evolution toward</span>
<span class="c"># finding individual who lie ON the circle.</span>
<span class="n">fitness</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">candidates</span><span class="p">:</span>
<span class="k">if</span> <span class="n">my_constraint_function</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">fitness</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">fitness</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">c</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="k">return</span> <span class="n">fitness</span>
<span class="k">def</span> <span class="nf">constrained_tournament_selection</span><span class="p">(</span><span class="n">random</span><span class="p">,</span> <span class="n">population</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">num_selected</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s">'num_selected'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">constraint_func</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s">'constraint_function'</span><span class="p">,</span> <span class="bp">None</span><span class="p">)</span>
<span class="n">tournament_size</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">pop</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">population</span><span class="p">)</span>
<span class="n">selected</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_selected</span><span class="p">):</span>
<span class="n">tournament</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">pop</span><span class="p">,</span> <span class="n">tournament_size</span><span class="p">)</span>
<span class="c"># If there is not a constraint function,</span>
<span class="c"># just do regular tournament selection.</span>
<span class="k">if</span> <span class="n">constraint_func</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">selected</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">tournament</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">cons</span> <span class="o">=</span> <span class="p">[</span><span class="n">constraint_func</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">candidate</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tournament</span><span class="p">]</span>
<span class="c"># If no constraints are violated, just do </span>
<span class="c"># regular tournament selection.</span>
<span class="k">if</span> <span class="nb">max</span><span class="p">(</span><span class="n">cons</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">selected</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">tournament</span><span class="p">))</span>
<span class="c"># Otherwise, choose the least violator </span>
<span class="c"># (which may be a non-violator).</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">selected</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tournament</span><span class="p">[</span><span class="n">cons</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">cons</span><span class="p">))])</span>
<span class="k">return</span> <span class="n">selected</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">Random</span><span class="p">()</span>
<span class="n">myec</span> <span class="o">=</span> <span class="n">ec</span><span class="o">.</span><span class="n">EvolutionaryComputation</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
<span class="n">myec</span><span class="o">.</span><span class="n">selector</span> <span class="o">=</span> <span class="n">constrained_tournament_selection</span>
<span class="n">myec</span><span class="o">.</span><span class="n">variator</span> <span class="o">=</span> <span class="n">variators</span><span class="o">.</span><span class="n">gaussian_mutation</span>
<span class="n">myec</span><span class="o">.</span><span class="n">replacer</span> <span class="o">=</span> <span class="n">replacers</span><span class="o">.</span><span class="n">generational_replacement</span>
<span class="n">myec</span><span class="o">.</span><span class="n">terminator</span> <span class="o">=</span> <span class="n">terminators</span><span class="o">.</span><span class="n">evaluation_termination</span>
<span class="n">myec</span><span class="o">.</span><span class="n">observer</span> <span class="o">=</span> <span class="n">observers</span><span class="o">.</span><span class="n">stats_observer</span>
<span class="n">pop</span> <span class="o">=</span> <span class="n">myec</span><span class="o">.</span><span class="n">evolve</span><span class="p">(</span><span class="n">my_generator</span><span class="p">,</span> <span class="n">my_evaluator</span><span class="p">,</span>
<span class="n">pop_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
<span class="n">bounder</span><span class="o">=</span><span class="n">ec</span><span class="o">.</span><span class="n">Bounder</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
<span class="n">num_selected</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
<span class="n">constraint_func</span><span class="o">=</span><span class="n">my_constraint_function</span><span class="p">,</span>
<span class="n">mutation_rate</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">max_evaluations</span><span class="o">=</span><span class="mi">2000</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span>
<span class="n">x</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">c</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">pop</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
<span class="n">num_feasible</span> <span class="o">=</span> <span class="nb">len</span><span class="p">([</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">pop</span> <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">fitness</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">])</span>
<span class="n">feasible_count</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pop</span><span class="p">):</span>
<span class="n">x</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">pop</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">c</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s">'r'</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">p</span><span class="o">.</span><span class="n">fitness</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">c</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s">'0.98'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">c</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">feasible_count</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">num_feasible</span><span class="p">)))</span>
<span class="n">feasible_count</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">angles</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">numpy</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">angles</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">angles</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s">'b'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">c</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s">'constraint_example.pdf'</span><span class="p">,</span> <span class="n">format</span><span class="o">=</span><span class="s">'pdf'</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="meta-evolutionary-computation">
<h2>Meta-Evolutionary Computation<a class="headerlink" href="#meta-evolutionary-computation" title="Permalink to this headline">¶</a></h2>
<p>The following recipe shows how an evolutionary computation can be used to evolve near-optimal operators and
parameters for another evolutionary computation. In the EC literature, such a thing is generally referred
to as a “meta-EC”.
[<a class="reference download internal" href="_downloads/meta_ec.py"><code class="xref download docutils literal"><span class="pre">download</span></code></a>]</p>
<div class="highlight-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">csv</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">from</span> <span class="nn">inspyred</span> <span class="kn">import</span> <span class="n">ec</span>
<span class="kn">from</span> <span class="nn">inspyred.ec</span> <span class="kn">import</span> <span class="n">selectors</span>
<span class="kn">from</span> <span class="nn">inspyred.ec</span> <span class="kn">import</span> <span class="n">replacers</span>
<span class="kn">from</span> <span class="nn">inspyred.ec</span> <span class="kn">import</span> <span class="n">variators</span>
<span class="kn">from</span> <span class="nn">inspyred.ec</span> <span class="kn">import</span> <span class="n">terminators</span>
<span class="kn">from</span> <span class="nn">inspyred.ec</span> <span class="kn">import</span> <span class="n">observers</span>
<span class="k">class</span> <span class="nc">MetaEC</span><span class="p">(</span><span class="n">ec</span><span class="o">.</span><span class="n">EvolutionaryComputation</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random</span><span class="p">):</span>
<span class="n">ec</span><span class="o">.</span><span class="n">EvolutionaryComputation</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">selector</span> <span class="o">=</span> <span class="n">selectors</span><span class="o">.</span><span class="n">tournament_selection</span>
<span class="bp">self</span><span class="o">.</span><span class="n">replacer</span> <span class="o">=</span> <span class="n">replacers</span><span class="o">.</span><span class="n">generational_replacement</span>
<span class="bp">self</span><span class="o">.</span><span class="n">variator</span> <span class="o">=</span> <span class="p">[</span><span class="n">variators</span><span class="o">.</span><span class="n">uniform_crossover</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_internal_variator</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">terminator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_internal_meta_terminator</span>
<span class="k">def</span> <span class="nf">_create_selector_replacer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random</span><span class="p">):</span>
<span class="n">pop_size</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">selector</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">replacer</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">sel</span> <span class="o">=</span> <span class="p">[</span><span class="n">selector</span><span class="p">]</span>
<span class="k">if</span> <span class="n">selector</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="k">if</span> <span class="n">replacer</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">replacer</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">or</span> <span class="n">replacer</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
<span class="n">sel</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pop_size</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">sel</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">pop_size</span><span class="p">))</span>
<span class="k">if</span> <span class="n">selector</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">sel</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">pop_size</span><span class="p">),</span> <span class="n">pop_size</span><span class="p">))</span>
<span class="n">rep</span> <span class="o">=</span> <span class="p">[</span><span class="n">replacer</span><span class="p">]</span>
<span class="k">if</span> <span class="n">replacer</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">rep</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">pop_size</span><span class="p">),</span> <span class="n">pop_size</span><span class="p">))</span>
<span class="k">elif</span> <span class="n">replacer</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">or</span> <span class="n">replacer</span> <span class="o">==</span> <span class="mi">5</span><span class="p">:</span>
<span class="n">rep</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">pop_size</span><span class="p">))</span>
<span class="k">return</span> <span class="p">[</span><span class="n">pop_size</span><span class="p">,</span> <span class="n">sel</span><span class="p">,</span> <span class="n">rep</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">_create_variators</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random</span><span class="p">):</span>
<span class="n">crossover</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
<span class="n">mutator</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
<span class="n">variators</span> <span class="o">=</span> <span class="p">([</span><span class="n">crossover</span><span class="p">],</span> <span class="p">[</span><span class="n">mutator</span><span class="p">])</span>
<span class="k">if</span> <span class="n">crossover</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">crossover</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
<span class="n">variators</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">())</span>
<span class="n">variators</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">())</span>
<span class="k">elif</span> <span class="n">crossover</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">variators</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">())</span>
<span class="k">elif</span> <span class="n">crossover</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">variators</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">())</span>
<span class="n">variators</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="k">elif</span> <span class="n">crossover</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
<span class="n">variators</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">30</span><span class="p">))</span>
<span class="k">if</span> <span class="n">mutator</span> <span class="o">==</span> <span class="mi">5</span><span class="p">:</span>
<span class="n">variators</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">())</span>
<span class="n">variators</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">())</span>
<span class="k">return</span> <span class="n">variators</span>
<span class="k">def</span> <span class="nf">_internal_generator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">cross</span><span class="p">,</span> <span class="n">mut</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_variators</span><span class="p">(</span><span class="n">random</span><span class="p">)</span>
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_create_selector_replacer</span><span class="p">(</span><span class="n">random</span><span class="p">),</span> <span class="n">cross</span><span class="p">,</span> <span class="n">mut</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">_internal_variator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random</span><span class="p">,</span> <span class="n">candidates</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">cs_copy</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">candidates</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">cs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">cs_copy</span><span class="p">):</span>
<span class="k">if</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o"><</span> <span class="mf">0.1</span><span class="p">:</span>
<span class="n">cs_copy</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_selector_replacer</span><span class="p">(</span><span class="n">random</span><span class="p">)</span>
<span class="k">if</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o"><</span> <span class="mf">0.1</span><span class="p">:</span>
<span class="n">cross</span><span class="p">,</span> <span class="n">mut</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_variators</span><span class="p">(</span><span class="n">random</span><span class="p">)</span>
<span class="n">cs_copy</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">cross</span>
<span class="n">cs_copy</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">mut</span>
<span class="k">return</span> <span class="n">cs_copy</span>
<span class="k">def</span> <span class="nf">_internal_observer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">population</span><span class="p">,</span> <span class="n">num_generations</span><span class="p">,</span> <span class="n">num_evaluations</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">population</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_observer_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s">'{0}, {1}, {2}</span><span class="se">\n</span><span class="s">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">p</span><span class="o">.</span><span class="n">fitness</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">candidate</span><span class="p">)))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_observer_file</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_internal_terminator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">population</span><span class="p">,</span> <span class="n">num_generations</span><span class="p">,</span> <span class="n">num_evaluations</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">maxevals</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">'max_evaluations'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_meta_evaluations</span> <span class="o">+=</span> <span class="n">num_evaluations</span>
<span class="k">return</span> <span class="n">num_evaluations</span> <span class="o">>=</span> <span class="n">maxevals</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_meta_evaluations</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_max_meta_evaluations</span>
<span class="k">def</span> <span class="nf">_internal_meta_terminator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">population</span><span class="p">,</span> <span class="n">num_generations</span><span class="p">,</span> <span class="n">num_evaluations</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_meta_evaluations</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_max_meta_evaluations</span>
<span class="k">def</span> <span class="nf">_internal_evaluator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">candidates</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">the_generator</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">'the_generator'</span><span class="p">)</span>
<span class="n">the_evaluator</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">'the_evaluator'</span><span class="p">)</span>
<span class="n">do_maximize</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">'do_maximize'</span><span class="p">,</span> <span class="bp">True</span><span class="p">)</span>
<span class="n">fitness</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">candidate</span> <span class="ow">in</span> <span class="n">candidates</span><span class="p">:</span>
<span class="n">popsize</span><span class="p">,</span> <span class="n">selector</span><span class="p">,</span> <span class="n">replacer</span><span class="p">,</span> <span class="n">crossover</span><span class="p">,</span> <span class="n">mutator</span><span class="p">,</span> <span class="n">myargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">interpret_candidate</span><span class="p">(</span><span class="n">candidate</span><span class="p">)</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'max_evaluations'</span><span class="p">]</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">'num_trial_evaluations'</span><span class="p">,</span> <span class="n">popsize</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">num_trials</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">'num_trials'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">evo</span> <span class="o">=</span> <span class="n">ec</span><span class="o">.</span><span class="n">EvolutionaryComputation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_random</span><span class="p">)</span>
<span class="n">evo</span><span class="o">.</span><span class="n">terminator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_internal_terminator</span>
<span class="n">evo</span><span class="o">.</span><span class="n">observer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_internal_observer</span>
<span class="n">evo</span><span class="o">.</span><span class="n">selector</span> <span class="o">=</span> <span class="n">selector</span>
<span class="n">evo</span><span class="o">.</span><span class="n">variator</span> <span class="o">=</span> <span class="p">[</span><span class="n">crossover</span><span class="p">,</span> <span class="n">mutator</span><span class="p">]</span>
<span class="n">evo</span><span class="o">.</span><span class="n">replacer</span> <span class="o">=</span> <span class="n">replacer</span>
<span class="n">best_fit</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_trials</span><span class="p">):</span>
<span class="n">final_pop</span> <span class="o">=</span> <span class="n">evo</span><span class="o">.</span><span class="n">evolve</span><span class="p">(</span><span class="n">generator</span><span class="o">=</span><span class="n">the_generator</span><span class="p">,</span>
<span class="n">evaluator</span><span class="o">=</span><span class="n">the_evaluator</span><span class="p">,</span>
<span class="n">pop_size</span><span class="o">=</span><span class="n">popsize</span><span class="p">,</span>
<span class="n">maximize</span><span class="o">=</span><span class="n">do_maximize</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="n">myargs</span><span class="p">)</span>
<span class="n">best_fit</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">final_pop</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">fitness</span><span class="p">)</span>
<span class="n">fitness</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">best_fit</span><span class="p">)</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">best_fit</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">fitness</span>
<span class="k">def</span> <span class="nf">interpret_candidate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">candidate</span><span class="p">):</span>
<span class="n">selector_mapping</span> <span class="o">=</span> <span class="p">(</span><span class="n">selectors</span><span class="o">.</span><span class="n">default_selection</span><span class="p">,</span>
<span class="n">selectors</span><span class="o">.</span><span class="n">rank_selection</span><span class="p">,</span>
<span class="n">selectors</span><span class="o">.</span><span class="n">tournament_selection</span><span class="p">,</span>
<span class="n">selectors</span><span class="o">.</span><span class="n">truncation_selection</span><span class="p">,</span>
<span class="n">selectors</span><span class="o">.</span><span class="n">uniform_selection</span><span class="p">)</span>
<span class="n">variator_mapping</span> <span class="o">=</span> <span class="p">(</span><span class="n">variators</span><span class="o">.</span><span class="n">blend_crossover</span><span class="p">,</span>
<span class="n">variators</span><span class="o">.</span><span class="n">heuristic_crossover</span><span class="p">,</span>
<span class="n">variators</span><span class="o">.</span><span class="n">n_point_crossover</span><span class="p">,</span>
<span class="n">variators</span><span class="o">.</span><span class="n">simulated_binary_crossover</span><span class="p">,</span>
<span class="n">variators</span><span class="o">.</span><span class="n">uniform_crossover</span><span class="p">,</span>
<span class="n">variators</span><span class="o">.</span><span class="n">gaussian_mutation</span><span class="p">,</span>
<span class="n">variators</span><span class="o">.</span><span class="n">default_variation</span><span class="p">)</span>
<span class="n">replacer_mapping</span> <span class="o">=</span> <span class="p">(</span><span class="n">replacers</span><span class="o">.</span><span class="n">comma_replacement</span><span class="p">,</span>
<span class="n">replacers</span><span class="o">.</span><span class="n">crowding_replacement</span><span class="p">,</span>
<span class="n">replacers</span><span class="o">.</span><span class="n">default_replacement</span><span class="p">,</span>
<span class="n">replacers</span><span class="o">.</span><span class="n">generational_replacement</span><span class="p">,</span>
<span class="n">replacers</span><span class="o">.</span><span class="n">plus_replacement</span><span class="p">,</span>
<span class="n">replacers</span><span class="o">.</span><span class="n">random_replacement</span><span class="p">,</span>
<span class="n">replacers</span><span class="o">.</span><span class="n">steady_state_replacement</span><span class="p">,</span>
<span class="n">replacers</span><span class="o">.</span><span class="n">truncation_replacement</span><span class="p">)</span>
<span class="n">myargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="c"># Selectors</span>
<span class="k">if</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'num_selected'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'num_selected'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'tournament_size'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'num_selected'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'num_selected'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="c"># Replacers</span>
<span class="k">if</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'crowding_distance'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'num_elites'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">5</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'num_elites'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="c"># Crossovers</span>
<span class="k">if</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'crossover_rate'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'blx_alpha'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'crossover_rate'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'crossover_rate'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'num_crossover_points'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'sbx_distribution_index'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'crossover_rate'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'ux_bias'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
<span class="c"># Mutators</span>
<span class="k">if</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">5</span><span class="p">:</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'mutation_rate'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="n">myargs</span><span class="p">[</span><span class="s">'gaussian_stdev'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
<span class="k">return</span> <span class="p">(</span><span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
<span class="n">selector_mapping</span><span class="p">[</span><span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]],</span>
<span class="n">replacer_mapping</span><span class="p">[</span><span class="n">candidate</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]],</span>
<span class="n">variator_mapping</span><span class="p">[</span><span class="n">candidate</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]],</span>
<span class="n">variator_mapping</span><span class="p">[</span><span class="n">candidate</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]],</span>
<span class="n">myargs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">evolve</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">generator</span><span class="p">,</span> <span class="n">evaluator</span><span class="p">,</span> <span class="n">pop_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">seeds</span><span class="o">=</span><span class="p">[],</span> <span class="n">maximize</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="o">**</span><span class="n">args</span><span class="p">):</span>
<span class="n">args</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s">'the_generator'</span><span class="p">,</span> <span class="n">generator</span><span class="p">)</span>
<span class="n">args</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s">'the_evaluator'</span><span class="p">,</span> <span class="n">evaluator</span><span class="p">)</span>
<span class="n">args</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s">'do_maximize'</span><span class="p">,</span> <span class="n">maximize</span><span class="p">)</span>
<span class="n">args</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s">'num_elites'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">args</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s">'num_selected'</span><span class="p">,</span> <span class="n">pop_size</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_observer_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s">'metaec-individuals-file-'</span> <span class="o">+</span> <span class="n">time</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s">'%m</span><span class="si">%d</span><span class="s">%Y-%H%M%S'</span><span class="p">)</span> <span class="o">+</span> <span class="s">'.csv'</span><span class="p">,</span> <span class="s">'w'</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_meta_evaluations</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_max_meta_evaluations</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">'max_evaluations'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">final_pop</span> <span class="o">=</span> <span class="n">ec</span><span class="o">.</span><span class="n">EvolutionaryComputation</span><span class="o">.</span><span class="n">evolve</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_internal_generator</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_internal_evaluator</span><span class="p">,</span> <span class="n">pop_size</span><span class="p">,</span>
<span class="n">seeds</span><span class="p">,</span> <span class="n">maximize</span><span class="p">,</span> <span class="o">**</span><span class="n">args</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_observer_file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="k">return</span> <span class="n">final_pop</span>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">'__main__'</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">inspyred</span>
<span class="n">prng</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">Random</span><span class="p">()</span>
<span class="n">prng</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">())</span>
<span class="n">problem</span> <span class="o">=</span> <span class="n">inspyred</span><span class="o">.</span><span class="n">benchmarks</span><span class="o">.</span><span class="n">Rastrigin</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">mec</span> <span class="o">=</span> <span class="n">MetaEC</span><span class="p">(</span><span class="n">prng</span><span class="p">)</span>
<span class="n">mec</span><span class="o">.</span><span class="n">observer</span> <span class="o">=</span> <span class="n">observers</span><span class="o">.</span><span class="n">stats_observer</span>
<span class="n">final_pop</span> <span class="o">=</span> <span class="n">mec</span><span class="o">.</span><span class="n">evolve</span><span class="p">(</span><span class="n">generator</span><span class="o">=</span><span class="n">problem</span><span class="o">.</span><span class="n">generator</span><span class="p">,</span>
<span class="n">evaluator</span><span class="o">=</span><span class="n">problem</span><span class="o">.</span><span class="n">evaluator</span><span class="p">,</span>
<span class="n">pop_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">maximize</span><span class="o">=</span><span class="n">problem</span><span class="o">.</span><span class="n">maximize</span><span class="p">,</span>
<span class="n">bounder</span><span class="o">=</span><span class="n">problem</span><span class="o">.</span><span class="n">bounder</span><span class="p">,</span>
<span class="n">num_trials</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">num_trial_evaluations</span><span class="o">=</span><span class="mi">5000</span><span class="p">,</span>
<span class="n">max_evaluations</span><span class="o">=</span><span class="mi">100000</span><span class="p">)</span>
<span class="n">pop_size</span><span class="p">,</span> <span class="n">selector</span><span class="p">,</span> <span class="n">replacer</span><span class="p">,</span> <span class="n">crossover</span><span class="p">,</span> <span class="n">mutator</span><span class="p">,</span> <span class="n">args</span> <span class="o">=</span> <span class="n">mec</span><span class="o">.</span><span class="n">interpret_candidate</span><span class="p">(</span><span class="n">final_pop</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">candidate</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Best Fitness: {0}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final_pop</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">fitness</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Population Size: {0}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pop_size</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Selector: {0}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">selector</span><span class="o">.</span><span class="n">__name__</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Replacer: {0}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">replacer</span><span class="o">.</span><span class="n">__name__</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Crossover: {0}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">crossover</span><span class="o">.</span><span class="n">__name__</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Mutator: {0}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mutator</span><span class="o">.</span><span class="n">__name__</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Parameters:'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s">' {0}: {1}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">args</span><span class="p">[</span><span class="n">key</span><span class="p">]))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Actual Evaluations Used: {0}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mec</span><span class="o">.</span><span class="n">_meta_evaluations</span><span class="p">))</span>
</pre></div>
</div>
</div>
<div class="section" id="micro-evolutionary-computation">
<h2>Micro-Evolutionary Computation<a class="headerlink" href="#micro-evolutionary-computation" title="Permalink to this headline">¶</a></h2>
<p>Another approach that has been successfully applied to some difficult problems is to use many small-population EC’s
for small numbers of evaluations in succession. Each succeeding EC is seeded with the best solution from the
previous run. This is somewhat akin to a random-restart hill-climbing approach, except that information about the
best solution so far is passed along during each restart.
[<a class="reference download internal" href="_downloads/micro_ec.py"><code class="xref download docutils literal"><span class="pre">download</span></code></a>]</p>
<div class="highlight-python"><div class="highlight"><pre>import collections
import inspyred
class MicroEC(inspyred.ec.EvolutionaryComputation):
def __init__(self, random):
inspyred.ec.EvolutionaryComputation.__init__(self, random)
def evolve(self, generator, evaluator, pop_size=10, seeds=None, maximize=True, bounder=None, **args):
self._kwargs = args
self._kwargs['_ec'] = self
if seeds is None:
seeds = []
if bounder is None:
bounder = inspyred.ec.Bounder()
self.termination_cause = None
self.generator = generator
self.evaluator = evaluator
self.bounder = bounder
self.maximize = maximize
self.population = []
self.archive = []
microseeds = seeds
while not self._should_terminate(list(self.population), self.num_generations, self.num_evaluations):
microec = inspyred.ec.EvolutionaryComputation(self._random)
microec.selector = self.selector
microec.variator = self.variator
microec.replacer = self.replacer
microec.observer = self.observer
microec.terminator = inspyred.ec.terminators.evaluation_termination
maxevals = args['max_evaluations']
args['max_evaluations'] = args['micro_evaluations']
result = microec.evolve(generator=generator, evaluator=evaluator,
pop_size=pop_size, seeds=microseeds,
maximize=maximize, **args)
self.population = list(result)
args['max_evaluations'] = maxevals
result.sort(reverse=True)
microseeds = [result[0].candidate]
self.num_evaluations += microec.num_evaluations
# Migrate individuals.
self.population = self.migrator(random=self._random,
population=self.population,
args=self._kwargs)
# Archive individuals.
self.archive = self.archiver(random=self._random, archive=self.archive,
population=list(self.population), args=self._kwargs)
self.num_generations += microec.num_generations
if isinstance(self.observer, collections.Iterable):
for obs in self.observer:
obs(population=list(self.population), num_generations=self.num_generations,
num_evaluations=self.num_evaluations, args=self._kwargs)
else:
self.observer(population=list(self.population), num_generations=self.num_generations,
num_evaluations=self.num_evaluations, args=self._kwargs)
return self.population
if __name__ == '__main__':
import random
import math
import time
def rastrigin_generator(random, args):
return [random.uniform(-5.12, 5.12) for _ in range(2)]
def rastrigin_evaluator(candidates, args):
fitness = []
for cand in candidates:
fitness.append(10 * len(cand) + sum([x**2 - 10 * (math.cos(2*math.pi*x)) for x in cand]))
return fitness
prng = random.Random()
prng.seed(time.time())
micro = MicroEC(prng)
micro.selector = inspyred.ec.selectors.tournament_selection
micro.replacer = inspyred.ec.replacers.steady_state_replacement
micro.variator = [inspyred.ec.variators.uniform_crossover, inspyred.ec.variators.gaussian_mutation]
micro.archiver = inspyred.ec.archivers.best_archiver
micro.observer = inspyred.ec.observers.stats_observer
micro.terminator = inspyred.ec.terminators.evaluation_termination
final_pop = micro.evolve(rastrigin_generator,
rastrigin_evaluator,
pop_size=10,
maximize=False,
bounder=inspyred.ec.Bounder(-5.12, 5.12),
max_evaluations=3000,
micro_evaluations=300,
num_selected=2,
gaussian_stdev=0.1)
print('Actual evaluations: {0}'.format(micro.num_evaluations))
for p in micro.archive:
print p
</pre></div>
</div>
</div>
<div class="section" id="network-migrator">
<h2>Network Migrator<a class="headerlink" href="#network-migrator" title="Permalink to this headline">¶</a></h2>
<p>The following custom migrator is a callable class (because the migrator must behave like a callback function)
that allows solutions to migrate from one network machine to another. It is assumed that the EC islands are
running on the given IP:port combinations.
[<a class="reference download internal" href="_downloads/network_migrator.py"><code class="xref download docutils literal"><span class="pre">download</span></code></a>]</p>
<div class="highlight-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">socket</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">threading</span>
<span class="kn">import</span> <span class="nn">collections</span>
<span class="kn">import</span> <span class="nn">SocketServer</span>
<span class="k">class</span> <span class="nc">NetworkMigrator</span><span class="p">(</span><span class="n">SocketServer</span><span class="o">.</span><span class="n">ThreadingMixIn</span><span class="p">,</span> <span class="n">SocketServer</span><span class="o">.</span><span class="n">TCPServer</span><span class="p">):</span>
<span class="sd">"""Defines a migration function across a network.</span>
<span class="sd"> </span>
<span class="sd"> This callable class acts as a migration function that </span>
<span class="sd"> allows candidate solutions to migrate from one population</span>
<span class="sd"> to another via TCP/IP connections.</span>
<span class="sd"> </span>
<span class="sd"> The migrator is constructed by specifying the IP address</span>
<span class="sd"> of the server (hosting the population from which individuals</span>
<span class="sd"> emigrate) as an IP-port tuple and the addresses of the clients </span>
<span class="sd"> (hosting the populations to which individuals from the server </span>
<span class="sd"> immigrate) as a list of IP-port tuples. The ``max_migrants`` </span>
<span class="sd"> parameter specifies the size of the queue of migrants waiting </span>
<span class="sd"> to immigrate to the server from the clients; the newest migrants </span>
<span class="sd"> replace older ones in the queue.</span>
<span class="sd"> </span>
<span class="sd"> Note: In order to use this migration operator, individuals</span>
<span class="sd"> must be pickle-able.</span>
<span class="sd"> </span>
<span class="sd"> The following is an example of the use of this operator::</span>
<span class="sd"> </span>
<span class="sd"> m = NetworkMigrator(('192.168.1.10', 25125),</span>
<span class="sd"> [('192.168.1.11', 12345), ('192.168.1.12', 54321)], </span>
<span class="sd"> max_migrants=3)</span>
<span class="sd"> </span>
<span class="sd"> Since the NetworkMigrator object is a server, it should always</span>
<span class="sd"> call the ``shutdown()`` method when it is no longer needed, in</span>
<span class="sd"> order to give back its resources.</span>
<span class="sd"> Public Attributes:</span>
<span class="sd"> </span>
<span class="sd"> - *client_addresses* -- the list of IP address tuples</span>
<span class="sd"> (IP, port) to which individuals should migrate</span>
<span class="sd"> - *migrants* -- the deque of migrants (of maximum size </span>
<span class="sd"> specified by ``max_migrants``) waiting to immigrate </span>
<span class="sd"> to client populations</span>
<span class="sd"> </span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">server_address</span><span class="p">,</span> <span class="n">client_addresses</span><span class="p">,</span> <span class="n">max_migrants</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_lock</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Lock</span><span class="p">()</span>
<span class="n">SocketServer</span><span class="o">.</span><span class="n">TCPServer</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">server_address</span><span class="p">,</span> <span class="bp">None</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">client_addresses</span> <span class="o">=</span> <span class="n">client_addresses</span>
<span class="bp">self</span><span class="o">.</span><span class="n">migrants</span> <span class="o">=</span> <span class="n">collections</span><span class="o">.</span><span class="n">deque</span><span class="p">(</span><span class="n">maxlen</span><span class="o">=</span><span class="n">max_migrants</span><span class="p">)</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Thread</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">serve_forever</span><span class="p">)</span>
<span class="n">t</span><span class="o">.</span><span class="n">setDaemon</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span>
<span class="n">t</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">__name__</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__class__</span><span class="o">.</span><span class="n">__name__</span>
<span class="k">def</span> <span class="nf">finish_request</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">request</span><span class="p">,</span> <span class="n">client_address</span><span class="p">):</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">rbufsize</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="n">wbufsize</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">rfile</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">makefile</span><span class="p">(</span><span class="s">'rb'</span><span class="p">,</span> <span class="n">rbufsize</span><span class="p">)</span>
<span class="n">wfile</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">makefile</span><span class="p">(</span><span class="s">'wb'</span><span class="p">,</span> <span class="n">wbufsize</span><span class="p">)</span>
<span class="n">pickle_data</span> <span class="o">=</span> <span class="n">rfile</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
<span class="n">migrant</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">pickle_data</span><span class="p">)</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lock</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">migrants</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">migrant</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">wfile</span><span class="o">.</span><span class="n">closed</span><span class="p">:</span>
<span class="n">wfile</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
<span class="n">wfile</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="n">rfile</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="k">finally</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exc_traceback</span> <span class="o">=</span> <span class="bp">None</span>
<span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random</span><span class="p">,</span> <span class="n">population</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="sd">"""Perform the migration.</span>
<span class="sd"> </span>
<span class="sd"> This function serves as the migration operator. Here, a random address</span>
<span class="sd"> is chosen from the ``client_addresses`` list, and a random individual</span>
<span class="sd"> is chosen from the population to become the migrant. A socket is opened</span>
<span class="sd"> to the chosen client address, and the chosen migrant is pickled and</span>
<span class="sd"> sent to the NetworkMigrator object running at the client address. Then</span>
<span class="sd"> the migrant queue on the current machine is queried for a migrant</span>
<span class="sd"> to replace the one sent. If one is found, it replaces the newly</span>
<span class="sd"> migrated individual; otherwise, the individual remains in the population.</span>
<span class="sd"> </span>
<span class="sd"> Any immigrants may also be re-evaluated before insertion into the</span>
<span class="sd"> current population by setting the ``evaluate_migrant`` keyword</span>
<span class="sd"> argument in ``args`` to True. This is useful if the evaluation</span>
<span class="sd"> functions in different populations are different and we want to compare</span>
<span class="sd"> "apples to apples," as they say.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> </span>
<span class="sd"> - *random* -- the random number generator object</span>
<span class="sd"> - *population* -- the population of Individuals</span>
<span class="sd"> - *args* -- a dictionary of keyword arguments</span>
<span class="sd"> </span>
<span class="sd"> Optional keyword arguments in the ``args`` parameter:</span>
<span class="sd"> </span>
<span class="sd"> - *evaluate_migrant* -- whether to re-evaluate the immigrant (default False)</span>
<span class="sd"> </span>
<span class="sd"> """</span>
<span class="n">evaluate_migrant</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s">'evaluate_migrant'</span><span class="p">,</span> <span class="bp">False</span><span class="p">)</span>
<span class="n">client_address</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">client_addresses</span><span class="p">)</span>
<span class="n">migrant_index</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">population</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">pickle_data</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">population</span><span class="p">[</span><span class="n">migrant_index</span><span class="p">])</span>
<span class="n">sock</span> <span class="o">=</span> <span class="n">socket</span><span class="o">.</span><span class="n">socket</span><span class="p">(</span><span class="n">socket</span><span class="o">.</span><span class="n">AF_INET</span><span class="p">,</span> <span class="n">socket</span><span class="o">.</span><span class="n">SOCK_STREAM</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">sock</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">client_address</span><span class="p">)</span>
<span class="n">sock</span><span class="o">.</span><span class="n">send</span><span class="p">(</span><span class="n">pickle_data</span> <span class="o">+</span> <span class="s">'</span><span class="se">\n</span><span class="s">'</span><span class="p">)</span>
<span class="k">finally</span><span class="p">:</span>
<span class="n">sock</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="n">migrant</span> <span class="o">=</span> <span class="bp">None</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lock</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">migrants</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">migrant</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">migrants</span><span class="o">.</span><span class="n">popleft</span><span class="p">()</span>
<span class="k">if</span> <span class="n">migrant</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
<span class="k">if</span> <span class="n">evaluate_migrant</span><span class="p">:</span>
<span class="n">fit</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">_ec</span><span class="o">.</span><span class="n">evaluator</span><span class="p">([</span><span class="n">migrant</span><span class="p">],</span> <span class="n">args</span><span class="p">)</span>
<span class="n">migrant</span><span class="o">.</span><span class="n">fitness</span> <span class="o">=</span> <span class="n">fit</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">args</span><span class="o">.</span><span class="n">_ec</span><span class="o">.</span><span class="n">num_evaluations</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">population</span><span class="p">[</span><span class="n">migrant_index</span><span class="p">]</span> <span class="o">=</span> <span class="n">migrant</span>
<span class="k">return</span> <span class="n">population</span>
<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">migrants</span><span class="p">)</span>
</pre></div>
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<h3><a href="index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">Recipes</a><ul>
<li><a class="reference internal" href="#lexicographic-ordering">Lexicographic Ordering</a></li>
<li><a class="reference internal" href="#constraint-selection">Constraint Selection</a></li>
<li><a class="reference internal" href="#meta-evolutionary-computation">Meta-Evolutionary Computation</a></li>
<li><a class="reference internal" href="#micro-evolutionary-computation">Micro-Evolutionary Computation</a></li>
<li><a class="reference internal" href="#network-migrator">Network Migrator</a></li>
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