Please check the**Contact Us** section for more contact details.

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

deffitness_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

defon_start(ga_instance):

print("on_start()")

defon_fitness(ga_instance,population_fitness):

print("on_fitness()")

defon_parents(ga_instance,selected_parents):

print("on_parents()")

defon_crossover(ga_instance,offspring_crossover):

print("on_crossover()")

defon_mutation(ga_instance,offspring_mutation):

print("on_mutation()")

defon_generation(ga_instance):

print("on_generation()")

defon_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.

Check the[PyGAD's documentation](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html) for information about the implementation of this example.