evolution-sim.py

#!/usr/bin/env python3
"""Minimal evolution simulator. Fitness-based selection over generations."""

import random

def evolve(population, fitness_fn, generations=10, mutation_rate=0.1):
    """Run evolution simulation."""
    for gen in range(generations):
        # Evaluate fitness
        scored = [(ind, fitness_fn(ind)) for ind in population]
        scored.sort(key=lambda x: x[1], reverse=True)

        # Select top 50%
        survivors = [ind for ind, _ in scored[:len(scored)//2]]

        # Reproduce
        offspring = []
        while len(offspring) < len(population) - len(survivors):
            parent = random.choice(survivors)
            child = mutate(parent, mutation_rate)
            offspring.append(child)

        population = survivors + offspring

        best_fitness = scored[0][1]
        print(f"Gen {gen}: best fitness = {best_fitness:.2f}")

    return population

def mutate(individual, rate):
    """Mutate individual with given probability."""
    return [gene + random.gauss(0, 0.1) if random.random() < rate else gene
            for gene in individual]

if __name__ == "__main__":
    # Example: evolve toward target [1, 2, 3, 4, 5]
    target = [1, 2, 3, 4, 5]

    def fitness(ind):
        return -sum((a - b)**2 for a, b in zip(ind, target))

    pop = [[random.random() * 10 for _ in range(5)] for _ in range(20)]
    final = evolve(pop, fitness, generations=50)

    print(f"\nBest individual: {final[0]}")
    print(f"Target: {target}")