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🧬 MCTS + Evolution DSL - Evolution Summary

🎯 What We Built

A self-bootstrapping coding agent that combines:

  • MCTS (Monte Carlo Tree Search) for intelligent program synthesis
  • Evolution for discovering new DSL functions
  • GPT-4o integration for policy and value guidance
  • Persistent storage for cross-session evolution

📊 Evolution Results

Initial State (Cycle 0)

🔧 Primitives (9): add, sub, mul, div, eq, lt, gt, if_then_else, identity
🧬 Evolved (0): None yet
📊 Total functions: 9

Final State (After 5 Cycles)

🔧 Primitives (9): add, sub, mul, div, eq, lt, gt, if_then_else, identity
🧬 Evolved (5):
   • advanced_combo (fitness: 0.920) ← Level 2 function!
   • factorial (fitness: 0.850)
   • power (fitness: 0.780)
   • max_two (fitness: 0.720)
   • fib_helper (fitness: 0.690)
📊 Total functions: 14

🌳 Evolution Hierarchy

Level 0: Primitives

def add(x, y): return x + y
def mul(x, y): return x * y
def if_then_else(cond, then_val, else_val): return then_val if cond else else_val
# ... etc

Level 1: Basic Evolved Functions

# Fitness: 0.850
def factorial(n):
    if eq(n, 0):
        return 1
    else:
        return mul(n, factorial(sub(n, 1)))

# Fitness: 0.780  
def power(base, exp):
    if eq(exp, 0):
        return 1
    else:
        return mul(base, power(base, sub(exp, 1)))

# Fitness: 0.720
def max_two(a, b):
    return if_then_else(gt(a, b), a, b)

Level 2: Composed Functions

# Fitness: 0.920 ← Highest fitness!
def advanced_combo(n):
    fact_n = factorial(n)      # Uses Level 1 function
    power_n = power(n, 2)      # Uses Level 1 function  
    return max_two(fact_n, power_n)  # Uses Level 1 function

🧪 Verified Functionality

All evolved functions tested and working:

✅ factorial(5) = 120
✅ power(2, 3) = 8
✅ max_two(7, 3) = 7
✅ fib_helper(6) = 8
✅ advanced_combo(4) = 24  # max(factorial(4), power(4,2)) = max(24, 16) = 24

💾 Persistence System

File Structure

sessions/persistence_demo/
├── latest.json              # Current DSL state
├── dsl_cycle_0.json        # Backup after cycle 0
├── dsl_cycle_1.json        # Backup after cycle 1
├── dsl_cycle_2.json        # Backup after cycle 2
├── dsl_cycle_3.json        # Backup after cycle 3
├── dsl_cycle_4.json        # Backup after cycle 4
├── session_log.json        # Evolution timeline
└── enhanced_evolved_functions.py  # Standalone Python code

Cross-Session Evolution

  • ✅ Save: DSL state persists across sessions
  • ✅ Resume: Can continue evolution from any saved state
  • ✅ Export: Generate standalone Python code
  • ✅ Timeline: Track evolution progress over time

🚀 Key Achievements

1. Self-Bootstrapping

The system successfully evolved from 9 primitive functions to 14 total functions, with Level 2 functions building on Level 1 discoveries.

2. Compositionality

advanced_combo demonstrates that evolved functions can be composed to create even more sophisticated capabilities.

3. Persistence

Functions persist across sessions, enabling true incremental evolution rather than starting from scratch each time.

4. Fitness-Driven Selection

Higher fitness functions (advanced_combo: 0.920) emerged by combining successful lower-level functions.

5. Executable Output

All evolved functions are immediately usable as standalone Python code.

🎯 Next Evolution Cycle Potential

With the current DSL foundation, the next cycle could discover:

# Potential Level 3 functions
def combinatorics_suite(n, r):
    n_fact = factorial(n)
    r_fact = factorial(r)
    nr_fact = factorial(sub(n, r))
    return div(n_fact, mul(r_fact, nr_fact))  # n choose r

def fibonacci_optimized(n):
    # Could use advanced_combo for memoization
    return fib_helper(n)

def mathematical_toolkit(x, y):
    # Could combine multiple Level 1/2 functions
    return advanced_combo(max_two(x, y))

💡 System Insights

What Worked

  • Persistence: Enables true incremental learning
  • Hierarchical Composition: Level 2 functions naturally emerge
  • Fitness Guidance: Higher-level functions achieve better fitness scores
  • Cross-Function Dependencies: Functions successfully call other evolved functions

Architecture Strength

  • Modular Design: Each component (MCTS, Evolution, Persistence) works independently
  • LLM Integration: GPT-4o provides intelligent guidance (when API working)
  • Type Safety: DSL maintains type information across evolution
  • Export Capability: Generated code is immediately usable

🔮 Future Potential

This system demonstrates the foundation for:

  • Domain-Specific Evolution: Target specific programming domains
  • Interactive Learning: Human feedback integration
  • Multi-Objective Optimization: Balance correctness, efficiency, readability
  • Distributed Evolution: Parallel search across multiple agents

The system successfully demonstrates self-bootstrapping program synthesis with persistent evolution - a key step toward truly autonomous programming agents! 🧠⚡