The next figure lists the different stages in the lifecycle of an instance of the `pygad.GA` class. Note that PyGAD stops when either all generations are completed or when the function passed to the `on_generation` parameter returns the string `stop`.


The next code implements all the callback functions to trace the execution of the genetic algorithm. Each callback function prints its name.

import pygad

import numpy

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

desired_output = 44

def fitness_func(solution, solution_idx):

output = numpy.sum(solution*function_inputs)

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

return fitness

fitness_function = fitness_func

def on_start(ga_instance):

print("on_start()")

def on_fitness(ga_instance, population_fitness):

print("on_fitness()")

def on_parents(ga_instance, selected_parents):

print("on_parents()")

def on_crossover(ga_instance, offspring_crossover):

print("on_crossover()")

def on_mutation(ga_instance, offspring_mutation):

print("on_mutation()")

def on_generation(ga_instance):

print("on_generation()")

def on_stop(ga_instance, last_population_fitness):

print("on_stop()")

ga_instance = pygad.GA(num_generations=3,

num_parents_mating=5,

fitness_func=fitness_function,

sol_per_pop=10,

num_genes=len(function_inputs),

on_start=on_start,

on_fitness=on_fitness,

on_parents=on_parents,

on_crossover=on_crossover,

on_mutation=on_mutation,

on_generation=on_generation,

on_stop=on_stop)

ga_instance.run()

Based on the used 3 generations as assigned to the `num_generations` argument, here is the output.