EMDE

EMDE (Efficient modified differential evolution)

class DETAlgs.emde.EMDE(params: EMDEData, db_conn=None, db_auto_write=False)[source]

Bases: BaseAlg

Links: https://link.springer.com/article/10.1007/s13042-015-0479-6

References: Mohamed, A.W. An efficient modified differential evolution algorithm for solving constrained non-linear integer and mixed-integer global optimization problems. Int. J. Mach. Learn. & Cyber. 8, 989–1007 (2017). https://doi.org/10.1007/s13042-015-0479-6

next_epoch()[source]

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.EMDEData(epoch: int = 100, population_size: int = 100, dimension: int = 10, lb: list = <factory>, ub: list = <factory>, mode: 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, crossover_rate: float = 0.1)[source]

Bases: BaseData

boundary_constraints_fun: BoundaryFixing = 'random'

crossover_rate: float = 0.1

dimension: int = 10

epoch: int = 100

function: FitnessFunctionBase = None

lb: list

log_population: bool = False

mode: OptimizationType = 'minimization'

parallel_processing: list | None = None

population_size: int = 100

ub: list