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Description
Please add explicitly in these tutorials whether it is a minimization or maximization problem.
https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/example_star_discr.ipynb
https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/example_sbox.ipynb
https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/example_submodular.ipynb
Add the explicit information in the tutorial example.
class RandomSearch:
'Simple random search algorithm'
def __init__(self, n: int, length: float = 0.0):
self.n: int = n
self.length: float = length
def __call__(self, problem: ioh.problem.RealSingleObjective) -> None:
'Evaluate the problem n times with a randomly generated solution'
best_fitness = 99999 <-- for minimisation
for _ in range(self.n):
# We can use the problems bounds accessor to get information about the problem bounds
x = np.random.uniform(problem.bounds.lb, problem.bounds.ub)
self.length = np.linalg.norm(x)
fitness = problem(x) <-- get fitness
if fitness < best_fitness: <-- explicit minimisation
best_fitness = fitness <-- explicit asignation
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