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How to save specific solutions based on conditions each generation #251
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Hi, I would like to save solutions at each generation based on a selection criteria that is different from the fitness function.
The goal of this process is to study some specific solutions, it has nothing to do with parents selection.
I know that there is the save_solutions argument but its take to much time to execute the model while saving all the solutions.
So my question is:
is there a way to filter between each generation the solutions i'm interrested and save them in an array ?
I've fought about using the on_generation argument but i'm struggling to implement this, bc it needs somme kind of global variable.
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Here is an example but it uses a global variable. It creates a list named filtered_solutions which only filters the solutions that have a fitness between 10 and 20, exclusive.
import pygad import numpy function_inputs = [4,-2,3.5,5,-11,-4.7] desired_output = 44 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 filtered_solutions = [] def on_generation(ga_instance): global filtered_solutions print(f"Generation = {ga_instance.generations_completed}") for idx in range(ga_instance.population.shape[0]): solution = ga_instance.population[idx] fitness = ga_instance.last_generation_fitness[idx] if 10 < fitness < 20: filtered_solutions.append(solution) ga_instance = pygad.GA(num_generations=50, num_parents_mating=5, sol_per_pop=10, num_genes=len(function_inputs), fitness_func=fitness_func, on_generation=on_generation, suppress_warnings=True) ga_instance.run()
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