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Copy pathExample_GeneticAlgorithm.py
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89 lines (73 loc) · 3.25 KB
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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, num_mutations=2)
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()