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https://github.com/kuhyx/WUT_Computer_Science.git
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58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
"""
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Program that optimizes Rastrigin function: f (x, y) =
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20 + (x^2 - 10cos(2πx)) + (y^2 - 10 cos(2πy)).
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Using Evolutionary Strategy (μ, λ).
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"""
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import numpy as np
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def rastrigin(x_argument, y_argument):
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""" Define the Rastrigin function """
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return 20 + x_argument**2 - 10 * np.cos(2 * np.pi * x_argument) + \
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y_argument**2 - 10 * np.cos(2 * np.pi * y_argument)
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def evolution_strategy(top_individuals, lambda_, sigma, iterations):
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""" Define the Evolutionary Strategy (μ, λ) algorithm """
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# Initialize the population
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population = np.random.uniform(low=-5.12, high=5.12, size=(lambda_, 2))
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# Iterate for a fixed number of iterations
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for i in range(iterations):
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# Evaluate the fitness of each individual
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fitness = np.array([rastrigin(x, y) for x, y in population])
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# Select the top top_individuals individuals
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parents = population[np.argsort(fitness)[:top_individuals]]
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# Generate the next generation of lambda individuals by recombination
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children = np.concatenate(
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[np.random.permutation(parents) for i in range(lambda_)])
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# Add mutation to the children
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mutation = np.random.normal(loc=0, scale=sigma, size=(lambda_, 2))
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population = children + mutation
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# Evaluate the fitness of the final population
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fitness = np.array([rastrigin(x, y) for x, y in population])
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# Return the best individual found
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best_idx = np.argmin(fitness)
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return population[best_idx], fitness[best_idx]
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# Ran first in the code
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if __name__ == "__main__":
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# Set the parameters
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MU = 5
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LAMBDA = 20
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SIGMA = 0.1
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ITERATIONS = 100
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# Run the Evolutionary Strategy algorithm
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best_individual, best_fitness = evolution_strategy(
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MU, LAMBDA, SIGMA, ITERATIONS)
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print("Best individual found:", best_individual)
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print("Best fitness found:", best_fitness)
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