More About PyGAD

This section covers the more advanced features of the pygad module. Pick a topic:

Multi-Objective Optimization

Optimize several objectives at once using NSGA-II or NSGA-III.

Multi-Objective Optimization

Controlling Gene Values

Restrict gene values with gene_space, gene_type, constraints, sample_size, and duplicate prevention.

Controlling Gene Values

Controlling Generations

Elitism, stopping criteria, random seed, saving and continuing, and population size.

Controlling Generations

Fitness Calculation and Performance

Parallel processing, batch fitness, reusing fitness, and non-deterministic problems.

Fitness Calculation and Performance

Logging and the Lifecycle Summary

Print a Keras-like summary and log the outputs.

Logging and the Lifecycle Summary

User-Defined Functions, Methods, and Classes

Pass your own functions, methods, or classes for the fitness and callbacks.

Use Functions, Methods, and Classes to Build Fitness and Callbacks

Benchmark Problems

Built-in single, multi, and many-objective benchmark problems to plug into the GA.

Benchmark Problems