A minimal evolutionary computing algorithm

minimal_ev_algo.py

import numpy as np
import matplotlib.pylab as plt


def f(x):
    return 50 - x**2


if __name__ == '__main__':
    np.random.seed(12345)

    PI = np.sqrt(2 / np.pi)
    M = 10  # population size
    pop = np.random.uniform(-100, 100, (M, 1))
    print(pop)
    print("===============")
    delta = 1.0
    fit = f(pop)
    
    births = 0
    for g in range(10000):
        parent = pop[np.random.randint(0, M)]
        offspring = parent.copy()
        # offspring +=  np.random.choice(np.array([-delta, delta]))
        offspring += np.random.normal(0, delta / PI, (1,))
        births += 1
        die = np.random.randint(0, M)
        if f(pop[die]) < f(offspring):
            pop[die] = offspring.copy()
        if births % 10 == 0:
            print(f(pop))
            print(pop)
            births = 0