ahmed-gad-ga.py

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 = 6



"""

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)



num_generations = 5

for generation in range(num_generations):

    print("Generation : ", generation)

    # Measing the fitness of each chromosome in the population.

    fitness = GA.cal_pop_fitness(equation_inputs, new_population)



    # Selecting the best parents in the population for mating.

    parents = GA.select_mating_pool(new_population, fitness, 

                                      num_parents_mating)



    # Generating next generation using crossover.

    offspring_crossover = GA.crossover(parents,

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



    # Adding some variations to the offsrping using mutation.

    offspring_mutation = GA.mutation(offspring_crossover)



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

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

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



    # The best result in the current iteration.

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



# 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])