GeneticAlgorithmPython/Example_GeneticAlgorithm.py at master · Abdelhek115/GeneticAlgorithmPython · GitHub

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import numpy

import GA

"""

The y=target is to maximize this equation ASAP:

y = w1x1+w2x2+w3x3+w4x4+w5x5+6wx6

where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7)

What are the best values for the 6 weights w1 to w6?

We are going to use the genetic algorithm for the best possible values after a number of generations.

"""

# Inputs of the equation.

equation_inputs = [4,-2,3.5,5,-11,-4.7]

# Number of the weights we are looking to optimize.

num_weights = len(equation_inputs)

"""

Genetic algorithm parameters:

Mating pool size

Population size

"""

sol_per_pop = 8

num_parents_mating = 4

# Defining the population size.

pop_size = (sol_per_pop,num_weights) # The population will have sol_per_pop chromosome where each chromosome has num_weights genes.

#Creating the initial population.

new_population = numpy.random.uniform(low=-4.0, high=4.0, size=pop_size)

print(new_population)

"""

new_population[0, :] = [2.4, 0.7, 8, -2, 5, 1.1]

new_population[1, :] = [-0.4, 2.7, 5, -1, 7, 0.1]

new_population[2, :] = [-1, 2, 2, -3, 2, 0.9]

new_population[3, :] = [4, 7, 12, 6.1, 1.4, -4]

new_population[4, :] = [3.1, 4, 0, 2.4, 4.8, 0]

new_population[5, :] = [-2, 3, -7, 6, 3, 3]

"""

best_outputs = []

num_generations = 1000

for generation in range(num_generations):

print("Generation : ", generation)

# Measuring the fitness of each chromosome in the population.

fitness = GA.cal_pop_fitness(equation_inputs, new_population)

print("Fitness")

print(fitness)

best_outputs.append(numpy.max(numpy.sum(new_population*equation_inputs, axis=1)))

# The best result in the current iteration.

print("Best result : ", numpy.max(numpy.sum(new_population*equation_inputs, axis=1)))

# Selecting the best parents in the population for mating.

parents = GA.select_mating_pool(new_population, fitness,

num_parents_mating)

print("Parents")

print(parents)

# Generating next generation using crossover.

offspring_crossover = GA.crossover(parents,

offspring_size=(pop_size[0]-parents.shape[0], num_weights))

print("Crossover")

print(offspring_crossover)

# Adding some variations to the offspring using mutation.

offspring_mutation = GA.mutation(offspring_crossover)

print("Mutation")

print(offspring_mutation)

# Creating the new population based on the parents and offspring.

new_population[0:parents.shape[0], :] = parents

new_population[parents.shape[0]:, :] = offspring_mutation

# Getting the best solution after iterating finishing all generations.

#At first, the fitness is calculated for each solution in the final generation.

fitness = GA.cal_pop_fitness(equation_inputs, new_population)

# Then return the index of that solution corresponding to the best fitness.

best_match_idx = numpy.where(fitness == numpy.max(fitness))

print("Best solution : ", new_population[best_match_idx, :])

print("Best solution fitness : ", fitness[best_match_idx])

import matplotlib.pyplot

matplotlib.pyplot.plot(best_outputs)

matplotlib.pyplot.xlabel("Iteration")

matplotlib.pyplot.ylabel("Fitness")

matplotlib.pyplot.show()

GeneticAlgorithmPython/Example_GeneticAlgorithm.py at master · Abdelhek115/GeneticAlgorithmPython · GitHub

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