DE

DE (Differential evolution)

class DETAlgs.de.DE(params: DEData, db_conn=None, db_auto_write=False)

Bases: BaseAlg

The original version of different evolution

Links: https://link.springer.com/article/10.1023/A:1008202821328

References: Storn, R., Price, K. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization 11, 341–359 (1997). https://doi.org/10.1023/A:1008202821328

next_epoch()

property nfe: int

Number of function evaluations performed so far.

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.DEData(population_size: int = 100, max_nfe: int = 100000, dimension: int = 10, additional_stop_criteria: detpy.models.stop_condition.stop_condition.StopCondition = <detpy.models.stop_condition.never_stop_condition.NeverStopCondition object at 0x7f136577ee40>, lb: list = <factory>, ub: list = <factory>, optimization_type: detpy.models.enums.optimization.OptimizationType = <OptimizationType.MINIMIZATION: 'minimization'>, boundary_constraints_fun: detpy.models.enums.boundary_constrain.BoundaryFixing = <BoundaryFixing.RANDOM: 'random'>, function: detpy.models.fitness_function.FitnessFunctionBase = None, log_population: bool = False, parallel_processing: Optional[list] = None, show_plots: bool = True, mutation_factor: float = 0.5, crossover_rate: float = 0.5, crossing_type: detpy.models.enums.crossingtype.CrossingType = <CrossingType.BINOMIAL: 'binomial'>, y: int = 1, base_vector_schema: detpy.models.enums.basevectorschema.BaseVectorSchema = <BaseVectorSchema.CURRENT: 'current'>)

Bases: BaseData

additional_stop_criteria: StopCondition = <detpy.models.stop_condition.never_stop_condition.NeverStopCondition object>

base_vector_schema: BaseVectorSchema = 'current'

boundary_constraints_fun: BoundaryFixing = 'random'

crossing_type: CrossingType = 'binomial'

crossover_rate: float = 0.5

dimension: int = 10

function: FitnessFunctionBase = None

lb: list

log_population: bool = False

max_nfe: int = 100000

mutation_factor: float = 0.5

optimization_type: OptimizationType = 'minimization'

parallel_processing: list | None = None

population_size: int = 100

show_plots: bool = True

ub: list

y: int = 1