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packages = [

"sqlite-utils", "pygad"

]

</py-config>

<h1>Pygad Example</h1>

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from __future__ import print_function, unicode_literals, division # if Python2.7

import sqlite_utils

import pygad

import numpy

"""

Given the following function:

y = f(w1:w6) = w1x1 + w2x2 + w3x3 + w4x4 + w5x5 + 6wx6

where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7) and y=44

What are the best values for the 6 weights (w1 to w6)? We are going to use the genetic algorithm to optimize this function.

"""

function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs.

desired_output = 44 # Function output.

def fitness_func(solution, solution_idx):

# Calculating the fitness value of each solution in the current population.

# The fitness function calulates the sum of products between each input and its corresponding weight.

output = numpy.sum(solution*function_inputs)

fitness = 1.0 / numpy.abs(output - desired_output)

return fitness

fitness_function = fitness_func

num_generations = 100 # Number of generations.

num_parents_mating = 7 # Number of solutions to be selected as parents in the mating pool.

# To prepare the initial population, there are 2 ways:

# 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population.

# 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless.

sol_per_pop = 50 # Number of solutions in the population.

num_genes = len(function_inputs)

last_fitness = 0

def callback_generation(ga_instance):

global last_fitness

print("Generation = {generation}".format(generation=ga_instance.generations_completed))

print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1]))

print("Change = {change}".format(change=ga_instance.best_solution()[1] - last_fitness))

last_fitness = ga_instance.best_solution()[1]

# Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor.

ga_instance = pygad.GA(num_generations=num_generations,

num_parents_mating=num_parents_mating,

fitness_func=fitness_function,

sol_per_pop=sol_per_pop,

num_genes=num_genes,

on_generation=callback_generation)

# Running the GA to optimize the parameters of the function.

ga_instance.run()

# After the generations complete, some plots are showed that summarize the how the outputs/fitenss values evolve over generations.

ga_instance.plot_fitness()

# Returning the details of the best solution.

solution, solution_fitness, solution_idx = ga_instance.best_solution()

print("Parameters of the best solution : {solution}".format(solution=solution))

print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))

print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))

prediction = numpy.sum(numpy.array(function_inputs)*solution)

print("Predicted output based on the best solution : {prediction}".format(prediction=prediction))

if ga_instance.best_solution_generation != -1:

print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation))

# Saving the GA instance.

filename = 'genetic' # The filename to which the instance is saved. The name is without extension.

ga_instance.save(filename=filename)

# Loading the saved GA instance.

loaded_ga_instance = pygad.load(filename=filename)

loaded_ga_instance.plot_fitness()

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