Genetic Algorithm

A population-based evolutionary algorithm with configurable mutation, selection, and recombination operators.

GeneticAlgorithm configuration plus its mutation, selection, and recombination operator types.

class RecombinatorType(*values)

Crossover strategies for combining parent solutions.

UNIFORM_CROSSOVER = 'Uniform crossover'

N_POINT_CROSSOVER = 'N-point crossover'

class ComparatorType(*values)

Solution-comparison strategy for multi-objective optimisation.

LEXICOGRAPHIC_COMPARATOR = 'Lexicographic comparator'

WEIGHTED_OBJECTIVE_COMPARATOR = 'Weighted objective lexicographic comparator'

SAMPLE_WEIGHTED_OBJECTIVE_COMPARATOR = 'Weighted objective online lexicographic comparator'

USER_WEIGHTED_OBJECTIVE_COMPARATOR = 'User weighted objective comparator'

LEXICOGRAPHIC_ONLINE_WEIGHTED_OBJECTIVE_COMPARATOR = 'Lexicographic online weighted objective comparator'

class MutationType(*values)

Mutation strategy used to perturb individual solutions.

GAUSSIAN_MUTATION = 'Gaussian mutation'

UNIFORM_MUTATION = 'Uniform mutation'

class SelectionType(*values)

Strategy for selecting individuals from the population.

TOURNAMENT_SELECTION = 'Tournament selection'

FAST_TOURNAMENT_SELECTION = 'Fast tournament selection'

RANDOM_SELECTION = 'Random selection'

class DistanceMetricType(*values)

Distance metric used for diversity-aware operators.

EUCLIDEAN_DISTANCE = 'Euclidean distance'

HAMMING_DISTANCE = 'Manhattan distance'

class SamplingMethodType(*values)

Method used to generate the initial population.

UNIFORM_RANDOM = 'Uniform random'

LATIN_HYPERCUBE = 'Latin Hypercube'

class Mutation(name, parameters)

A mutation operator: a MutationType plus its operator-specific parameters.

name: MutationType

parameters: dict[str, Any]

classmethod mutation(name=None, variation_range=None)

Construct a Mutation with default operator parameters.

Parameters:

• name (MutationType | None) – Mutation strategy. Defaults to UNIFORM_MUTATION.

• variation_range (float | NumericExpression | None) – Variation magnitude for uniform mutation. Must be non-negative.

Return type:

Mutation

class Selection(name, parameters)

A selection operator: a SelectionType plus its operator-specific parameters.

name: SelectionType

parameters: dict[str, Any]

classmethod selection(name=None, pool_size=None)

Construct a Selection with default operator parameters.

Parameters:

• name (SelectionType | None) – Selection strategy. Defaults to FAST_TOURNAMENT_SELECTION.

• pool_size (int | NumericExpression | None) – Tournament pool size. Must be at least 2.

Return type:

Selection

class GeneticAlgorithm(log_info=False, evaluations=100000, max_evaluations_without_improvement=10000, max_time_without_improvement=300, min_improvement=1e-06, max_restart_count=0, prng_seed=None, time_limit=60, mutation_rate=(1 / NUM_VARIABLES), recombinator_rate=0.8, population_size=NUM_VARIABLES, elitism=1, mutation=<factory>, recombinator=RecombinatorType.UNIFORM_CROSSOVER, n_point_cuts=None, selection=<factory>, comparator=None, tiebreaker=False, distance_metric=DistanceMetricType.EUCLIDEAN_DISTANCE, sample_count=0, sampling_method=SamplingMethodType.UNIFORM_RANDOM)

Population-based evolutionary algorithm.

mutation_rate: float | NumericExpression | Parameter | Calculation = (1 / NUM_VARIABLES)

Probability of mutating an offspring (0–1).

recombinator_rate: float | NumericExpression | Parameter | Calculation = 0.8

Probability of applying crossover (0–1).

population_size: int | NumericExpression | Parameter | Calculation = NUM_VARIABLES

Number of individuals in the population.

elitism: int | NumericExpression | Parameter | Calculation = 1

Number of top individuals copied unchanged into the next generation.

mutation: Mutation

Mutation operator.

recombinator: RecombinatorType = 'Uniform crossover'

Crossover strategy.

n_point_cuts: int | NumericExpression | Parameter | Calculation | None = None

Number of crossover points; auto-set for N-point crossover.

selection: Selection

Selection operator.

comparator: ComparatorType | None = None

Optional solution comparator for multi-objective runs.

tiebreaker: bool = False

Apply a diversity-aware tiebreaker on equal objective values.

distance_metric: DistanceMetricType = 'Euclidean distance'

Distance metric for diversity-aware operators.

sample_count: int | NumericExpression | Parameter | Calculation = 0

Initial-sample count for sampling-based seeding.

sampling_method: SamplingMethodType = 'Uniform random'

Initial-population sampling method.

property key: str

The algorithm key emitted as algorithmKey in the serialised output.