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

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:::{grid-item-card} Multi-Objective Optimization :link: multi_objective :link-type: doc

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

:::{grid-item-card} Controlling Gene Values :link: gene_values :link-type: doc

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

:::{grid-item-card} Controlling Generations :link: generations :link-type: doc

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

:::{grid-item-card} Fitness Calculation and Performance :link: fitness_calculation :link-type: doc

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

:::{grid-item-card} Logging and the Lifecycle Summary :link: logging :link-type: doc

Print a Keras-like summary and log the outputs. :::

:::{grid-item-card} User-Defined Functions, Methods, and Classes :link: custom_functions :link-type: doc

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

:::{grid-item-card} Benchmark Problems :link: benchmarks :link-type: doc

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

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multi_objective gene_values generations fitness_calculation logging custom_functions benchmarks :::