Open
Description
The following is my code for pygad:
num_parents_mating = 4
parent_selection_type = "random"
keep_parents = -1
crossover_type = "single_point"
mutation_type = "random"
mutation_percent_genes = 10
num_edge_pairs_per_edges = 5.0
drop_single_nodes = True
ga_instance = pygad.GA(num_generations=num_generations,
num_parents_mating=num_parents_mating,
fitness_func=problem.fitness_func,
parent_selection_type=parent_selection_type,
keep_parents=keep_parents,
crossover_type=crossover_type,
mutation_type=mutation_type,
mutation_percent_genes=mutation_percent_genes,
random_seed=100500,
gene_type=int,
keep_elitism=0,
initial_population=initial_population,
stop_criteria=['saturate_15'],
logger=logger,
save_solutions=False,
on_fitness=on_fitness,
)problem is class instance, it is computing fitness as well as logging and saving some data. Best solution and best fitness are stored in best_solution_cls and best_fitness_cls attributes respectively. There is a strange bug in my code, that best_solution_cls doesn't match to best_fitness_cls. While best_fitness_cls is indeed the best fitness across all tries, best_fitness_cls is some other solution, it is usually not even valid (for invalid solutions, fitness=-1000.0 is expected).
The following is conda_env.yml used to create environment:
channels:
- pytorch
- anaconda
- conda-forge
- defaults
dependencies:
- python=3.7.15=haa1d7c7_0
- pip=22.2.2=py37h06a4308_0
- setuptools=65.5.0=py37h06a4308_0
- pip:
- numpy==1.21.6
- pandas==1.3.5
- scipy==1.7.3
- tqdm==4.64.1
- networkx==2.6.3
- numba==0.56.4
- pygad==3.2.0