Pygad optimizer

.tools/envs/testenv-linux.yml

@@ -28,6 +28,7 @@ dependencies:
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
   - iminuit  # dev, tests
+  - pygad  # dev, tests
   - pip:  # dev, tests, docs
       - nevergrad  # dev, tests
       - DFO-LS>=1.5.3  # dev, tests

.tools/envs/testenv-numpy.yml

@@ -26,6 +26,7 @@ dependencies:
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
   - iminuit  # dev, tests
+  - pygad  # dev, tests
   - pip:  # dev, tests, docs
       - nevergrad  # dev, tests
       - DFO-LS>=1.5.3  # dev, tests

.tools/envs/testenv-others.yml

@@ -26,6 +26,7 @@ dependencies:
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
   - iminuit  # dev, tests
+  - pygad  # dev, tests
   - pip:  # dev, tests, docs
       - nevergrad  # dev, tests
       - DFO-LS>=1.5.3  # dev, tests

.tools/envs/testenv-pandas.yml

@@ -26,6 +26,7 @@ dependencies:
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
   - iminuit  # dev, tests
+  - pygad  # dev, tests
   - pip:  # dev, tests, docs
       - nevergrad  # dev, tests
       - DFO-LS>=1.5.3  # dev, tests

.tools/envs/testenv-plotly.yml

@@ -26,6 +26,7 @@ dependencies:
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
   - iminuit  # dev, tests
+  - pygad  # dev, tests
   - pip:  # dev, tests, docs
       - nevergrad  # dev, tests
       - DFO-LS>=1.5.3  # dev, tests

docs/source/algorithms.md

@@ -4043,6 +4043,63 @@ these optimizers, you need to have
       initialization for speed. Default is False.
 ```
 
+## PyGAD Optimizer
+
+optimagic supports the [PyGAD](https://github.com/ahmedfgad/GeneticAlgorithmPython)
+genetic algorithm optimizer. To use PyGAD, you need to have
+[the pygad package](https://github.com/ahmedfgad/GeneticAlgorithmPython) installed
+(`pip install pygad`).
+
+```{eval-rst}
+.. dropdown:: pygad
+
+    .. code-block::
+
+        "pygad"
+
+    Minimize a scalar function using the PyGAD genetic algorithm.
+
+    PyGAD is a Python library for building genetic algorithms and training machine learning algorithms.
+    Genetic algorithms are metaheuristics inspired by the process of natural selection that belong to
+    the larger class of evolutionary algorithms. These algorithms apply biologically inspired
+    operators such as mutation, crossover, and selection to optimization problems.
+
+    The algorithm maintains a population of candidate solutions and iteratively improves them
+    through genetic operations, making it ideal for global optimization problems with complex
+    search spaces that may contain multiple local optima.
+
+    The algorithm supports the following options:
+
+    - **population_size** (int): Number of solutions in each generation. Default is None.
+    - **num_parents_mating** (int): Number of parents selected for mating in each generation. Default is None.
+    - **num_generations** (int): Number of generations. Default is None.
+    - **initial_population** (array-like): initial population is a 2D array where
+      each row represents a solution and each column represents a parameter (gene) value.
+      The number of rows must equal population_size, and the number of columns must
+      match the length of the initial parameters (x0).
+      When None, the population is randomly generated within the parameter bounds using
+      the specified population_size and the dimensionality from x0.
+    - **parent_selection_type** (str or callable): Method for selecting parents. Can be a string ("sss", "rws", "sus", "rank", "random", "tournament") or a custom function with signature ``parent_selection_func(fitness, num_parents, ga_instance) -> tuple[NDArray, NDArray]``. Default is "sss".
+    - **keep_parents** (int): Number of best parents to keep in the next generation. Only has effect when keep_elitism is 0. Default is -1.
+    - **keep_elitism** (int): Number of best solutions to preserve across generations. If non-zero, keep_parents has no effect. Default is 1.
+    - **K_tournament** (int): Tournament size for tournament selection. Only used when parent_selection_type is "tournament". Default is 3.
+    - **crossover_type** (str, callable, or None): Crossover method. Can be a string ("single_point", "two_points", "uniform", "scattered"), a custom function with signature ``crossover_func(parents, offspring_size, ga_instance) -> NDArray``, or None to disable crossover. Default is "single_point".
+    - **crossover_probability** (float): Probability of applying crossover. Range [0, 1]. Default is None.
+    - **mutation_type** (str, callable, or None): Mutation method. Can be a string ("random", "swap", "inversion", "scramble", "adaptive"), a custom function with signature ``mutation_func(offspring, ga_instance) -> NDArray``, or None to disable mutation. Default is "random".
+    - **mutation_probability** (float/list/tuple/array): Probability of mutation. Range [0, 1]. If specified, mutation_percent_genes and mutation_num_genes are ignored. Default is None.
+    - **mutation_percent_genes** (float/str/list/tuple/array): Percentage of genes to mutate. Default is "default" (equivalent to 10%). Ignored if mutation_probability or mutation_num_genes are specified.
+    - **mutation_num_genes** (int/list/tuple/array): Exact number of genes to mutate. Ignored if mutation_probability is specified. Default is None.
+    - **mutation_by_replacement** (bool): Whether to replace gene values during mutation. Only works with mutation_type="random". Default is False.
+    - **random_mutation_min_val** (float/list/array): Minimum value for random mutation. Only used with mutation_type="random". Default is -1.0.
+    - **random_mutation_max_val** (float/list/array): Maximum value for random mutation. Only used with mutation_type="random". Default is 1.0.
+    - **allow_duplicate_genes** (bool): Whether to allow duplicate gene values within a solution. Default is True.
+    - **fitness_batch_size** (int): Number of solutions to evaluate in parallel batches. When None and n_cores > 1, automatically set to n_cores for optimal parallelization. Default is None.
+    - **stop_criteria** (str/list): Early stopping criteria. Format: "reach_value" or "saturate_N". Default is None.
+    - **n_cores** (int): Number of cores for parallel fitness evaluation. Default is 1.
+    - **random_seed** (int): Random seed for reproducibility. Default is None.
+
+```
+
 ## References
 
 ```{eval-rst}

environment.yml

@@ -38,6 +38,7 @@ dependencies:
   - furo  # dev, docs
   - annotated-types  # dev, tests
   - iminuit  # dev, tests
+  - pygad  # dev, tests
   - pip:  # dev, tests, docs
       - nevergrad  # dev, tests
       - DFO-LS>=1.5.3  # dev, tests

pyproject.toml

@@ -381,5 +381,6 @@ module = [
     "pdbp",
     "iminuit",
     "nevergrad",
+    "pygad",
   ]
 ignore_missing_imports = true

src/optimagic/algorithms.py

@@ -38,6 +38,7 @@
     NloptVAR,
 )
 from optimagic.optimizers.pounders import Pounders
+from optimagic.optimizers.pygad_optimizer import Pygad
 from optimagic.optimizers.pygmo_optimizers import (
     PygmoBeeColony,
     PygmoCmaes,
@@ -173,6 +174,7 @@ def Scalar(
 @dataclass(frozen=True)
 class BoundedGlobalGradientFreeParallelScalarAlgorithms(AlgoSelection):
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -371,6 +373,7 @@ class BoundedGlobalGradientFreeScalarAlgorithms(AlgoSelection):
     nlopt_direct: Type[NloptDirect] = NloptDirect
     nlopt_esch: Type[NloptESCH] = NloptESCH
     nlopt_isres: Type[NloptISRES] = NloptISRES
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -407,6 +410,7 @@ def Parallel(self) -> BoundedGlobalGradientFreeParallelScalarAlgorithms:
 @dataclass(frozen=True)
 class BoundedGlobalGradientFreeParallelAlgorithms(AlgoSelection):
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -463,6 +467,7 @@ def Scalar(self) -> GlobalGradientFreeNonlinearConstrainedParallelScalarAlgorith
 @dataclass(frozen=True)
 class GlobalGradientFreeParallelScalarAlgorithms(AlgoSelection):
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -611,6 +616,7 @@ def Scalar(self) -> BoundedGradientFreeNonlinearConstrainedParallelScalarAlgorit
 @dataclass(frozen=True)
 class BoundedGradientFreeParallelScalarAlgorithms(AlgoSelection):
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -706,6 +712,7 @@ def Scalar(self) -> BoundedGlobalNonlinearConstrainedParallelScalarAlgorithms:
 @dataclass(frozen=True)
 class BoundedGlobalParallelScalarAlgorithms(AlgoSelection):
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -1037,6 +1044,7 @@ class BoundedGlobalGradientFreeAlgorithms(AlgoSelection):
     nlopt_direct: Type[NloptDirect] = NloptDirect
     nlopt_esch: Type[NloptESCH] = NloptESCH
     nlopt_isres: Type[NloptISRES] = NloptISRES
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -1101,6 +1109,7 @@ class GlobalGradientFreeScalarAlgorithms(AlgoSelection):
     nlopt_direct: Type[NloptDirect] = NloptDirect
     nlopt_esch: Type[NloptESCH] = NloptESCH
     nlopt_isres: Type[NloptISRES] = NloptISRES
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -1141,6 +1150,7 @@ def Parallel(self) -> GlobalGradientFreeParallelScalarAlgorithms:
 @dataclass(frozen=True)
 class GlobalGradientFreeParallelAlgorithms(AlgoSelection):
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -1316,6 +1326,7 @@ class BoundedGradientFreeScalarAlgorithms(AlgoSelection):
     nlopt_newuoa: Type[NloptNEWUOA] = NloptNEWUOA
     nlopt_neldermead: Type[NloptNelderMead] = NloptNelderMead
     nlopt_sbplx: Type[NloptSbplx] = NloptSbplx
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -1380,6 +1391,7 @@ def Parallel(self) -> BoundedGradientFreeLeastSquaresParallelAlgorithms:
 class BoundedGradientFreeParallelAlgorithms(AlgoSelection):
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     pounders: Type[Pounders] = Pounders
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -1461,6 +1473,7 @@ def Scalar(self) -> GradientFreeNonlinearConstrainedParallelScalarAlgorithms:
 class GradientFreeParallelScalarAlgorithms(AlgoSelection):
     neldermead_parallel: Type[NelderMeadParallel] = NelderMeadParallel
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -1534,6 +1547,7 @@ class BoundedGlobalScalarAlgorithms(AlgoSelection):
     nlopt_direct: Type[NloptDirect] = NloptDirect
     nlopt_esch: Type[NloptESCH] = NloptESCH
     nlopt_isres: Type[NloptISRES] = NloptISRES
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -1579,6 +1593,7 @@ def Parallel(self) -> BoundedGlobalParallelScalarAlgorithms:
 @dataclass(frozen=True)
 class BoundedGlobalParallelAlgorithms(AlgoSelection):
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -1648,6 +1663,7 @@ def Scalar(self) -> GlobalNonlinearConstrainedParallelScalarAlgorithms:
 @dataclass(frozen=True)
 class GlobalParallelScalarAlgorithms(AlgoSelection):
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -1883,6 +1899,7 @@ def Scalar(self) -> BoundedNonlinearConstrainedParallelScalarAlgorithms:
 @dataclass(frozen=True)
 class BoundedParallelScalarAlgorithms(AlgoSelection):
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -2146,6 +2163,7 @@ class GlobalGradientFreeAlgorithms(AlgoSelection):
     nlopt_direct: Type[NloptDirect] = NloptDirect
     nlopt_esch: Type[NloptESCH] = NloptESCH
     nlopt_isres: Type[NloptISRES] = NloptISRES
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -2240,6 +2258,7 @@ class BoundedGradientFreeAlgorithms(AlgoSelection):
     nlopt_neldermead: Type[NloptNelderMead] = NloptNelderMead
     nlopt_sbplx: Type[NloptSbplx] = NloptSbplx
     pounders: Type[Pounders] = Pounders
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -2337,6 +2356,7 @@ class GradientFreeScalarAlgorithms(AlgoSelection):
     nlopt_neldermead: Type[NloptNelderMead] = NloptNelderMead
     nlopt_praxis: Type[NloptPRAXIS] = NloptPRAXIS
     nlopt_sbplx: Type[NloptSbplx] = NloptSbplx
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -2409,6 +2429,7 @@ class GradientFreeParallelAlgorithms(AlgoSelection):
     neldermead_parallel: Type[NelderMeadParallel] = NelderMeadParallel
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     pounders: Type[Pounders] = Pounders
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -2452,6 +2473,7 @@ class BoundedGlobalAlgorithms(AlgoSelection):
     nlopt_direct: Type[NloptDirect] = NloptDirect
     nlopt_esch: Type[NloptESCH] = NloptESCH
     nlopt_isres: Type[NloptISRES] = NloptISRES
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -2534,6 +2556,7 @@ class GlobalScalarAlgorithms(AlgoSelection):
     nlopt_direct: Type[NloptDirect] = NloptDirect
     nlopt_esch: Type[NloptESCH] = NloptESCH
     nlopt_isres: Type[NloptISRES] = NloptISRES
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -2583,6 +2606,7 @@ def Parallel(self) -> GlobalParallelScalarAlgorithms:
 @dataclass(frozen=True)
 class GlobalParallelAlgorithms(AlgoSelection):
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -2863,6 +2887,7 @@ class BoundedScalarAlgorithms(AlgoSelection):
     nlopt_sbplx: Type[NloptSbplx] = NloptSbplx
     nlopt_tnewton: Type[NloptTNewton] = NloptTNewton
     nlopt_var: Type[NloptVAR] = NloptVAR
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -2950,6 +2975,7 @@ def Parallel(self) -> BoundedLeastSquaresParallelAlgorithms:
 class BoundedParallelAlgorithms(AlgoSelection):
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     pounders: Type[Pounders] = Pounders
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -3051,6 +3077,7 @@ def Scalar(self) -> NonlinearConstrainedParallelScalarAlgorithms:
 class ParallelScalarAlgorithms(AlgoSelection):
     neldermead_parallel: Type[NelderMeadParallel] = NelderMeadParallel
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -3170,6 +3197,7 @@ class GradientFreeAlgorithms(AlgoSelection):
     nlopt_praxis: Type[NloptPRAXIS] = NloptPRAXIS
     nlopt_sbplx: Type[NloptSbplx] = NloptSbplx
     pounders: Type[Pounders] = Pounders
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -3234,6 +3262,7 @@ class GlobalAlgorithms(AlgoSelection):
     nlopt_direct: Type[NloptDirect] = NloptDirect
     nlopt_esch: Type[NloptESCH] = NloptESCH
     nlopt_isres: Type[NloptISRES] = NloptISRES
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -3380,6 +3409,7 @@ class BoundedAlgorithms(AlgoSelection):
     nlopt_tnewton: Type[NloptTNewton] = NloptTNewton
     nlopt_var: Type[NloptVAR] = NloptVAR
     pounders: Type[Pounders] = Pounders
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -3517,6 +3547,7 @@ class ScalarAlgorithms(AlgoSelection):
     nlopt_sbplx: Type[NloptSbplx] = NloptSbplx
     nlopt_tnewton: Type[NloptTNewton] = NloptTNewton
     nlopt_var: Type[NloptVAR] = NloptVAR
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch
@@ -3631,6 +3662,7 @@ class ParallelAlgorithms(AlgoSelection):
     neldermead_parallel: Type[NelderMeadParallel] = NelderMeadParallel
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     pounders: Type[Pounders] = Pounders
+    pygad: Type[Pygad] = Pygad
     pygmo_gaco: Type[PygmoGaco] = PygmoGaco
     pygmo_pso_gen: Type[PygmoPsoGen] = PygmoPsoGen
     scipy_brute: Type[ScipyBrute] = ScipyBrute
@@ -3696,6 +3728,7 @@ class Algorithms(AlgoSelection):
     nlopt_tnewton: Type[NloptTNewton] = NloptTNewton
     nlopt_var: Type[NloptVAR] = NloptVAR
     pounders: Type[Pounders] = Pounders
+    pygad: Type[Pygad] = Pygad
     pygmo_bee_colony: Type[PygmoBeeColony] = PygmoBeeColony
     pygmo_cmaes: Type[PygmoCmaes] = PygmoCmaes
     pygmo_compass_search: Type[PygmoCompassSearch] = PygmoCompassSearch