Simple genetical algorythm solving binomial equation.

gen.py

"""
find foo(x, y, z) == 25
"""
import random
from itertools import chain

MAX_GENERATIONS = 10_000
GEN_POOL_SIZE = 1_000
BEST_N = 100


def foo(x, y, z):
    return 6 * x ** 4 + 9 * y ** 2 + 90 * z - 25


def fitness(x, y, z):
    """rank the fitness of the solution"""
    answer = foo(x, y, z)
    return 99999 if answer == 0 else abs(1 / answer)


# generate random solutions
def rand_tuple():
    return tuple(random.uniform(0, 10_000) for _ in range(3))


solutions = [rand_tuple() for _ in range(GEN_POOL_SIZE)]


for gen in range(MAX_GENERATIONS):

    # evaluate and sort solutions
    ranked_solutions = sorted(
        ((fitness(*sol), sol) for sol in solutions),
        key=lambda x: x[0],
        reverse=True
        )
    
    # take n best solutions
    best_solutions = ranked_solutions[:BEST_N]

    if best_solutions[0][0] > 999:
        result = best_solutions[0]
        print(f'{gen=}')
        print(f'{result=}')
        print(f'foo(result) = {foo(*result[1])}')
        break

    # combine solutions
    parameter_pool = list(chain(
        *(sol[1] for sol in best_solutions)
        ))
    
    # mutate and create a new generation
    new_gen = [
        [random.choice(parameter_pool) * random.uniform(0.99, 1.01)
         for _ in range(3)]
        for _ in range(GEN_POOL_SIZE)
        ]

    solutions = new_gen