GeneticAlgorithmPython/examples/example.py at update_fstring · KelvinChung2000/GeneticAlgorithmPython · GitHub

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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(ga_instance, solution, solution_idx):

output = numpy.sum(solution*function_inputs)

fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)

return fitness

num_generations = 100 # Number of generations.

num_parents_mating = 10 # Number of solutions to be selected as parents in the mating pool.

sol_per_pop = 20 # Number of solutions in the population.

num_genes = len(function_inputs)

last_fitness = 0

def on_generation(ga_instance):

global last_fitness

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

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

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

last_fitness = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1]

ga_instance = pygad.GA(num_generations=num_generations,

num_parents_mating=num_parents_mating,

sol_per_pop=sol_per_pop,

num_genes=num_genes,

fitness_func=fitness_func,

on_generation=on_generation)

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

ga_instance.run()

ga_instance.plot_fitness()

# Returning the details of the best solution.

solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness)

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()

GeneticAlgorithmPython/examples/example.py at update_fstring · KelvinChung2000/GeneticAlgorithmPython · GitHub

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