EPSDEG

EPSDEG (Epsilon Constrained Differential Evolution with Gradient-Based Mutation)

class detpy.DETAlgs.eps_deg.EPSDEG(params: EPSDEGData, db_conn=None, db_auto_write=False)

Bases: BaseAlg

EPSDEG - Epsilon Constrained Differential Evolution with Gradient-Based Mutation

Links: https://link.springer.com/chapter/10.1007/978-3-642-00619-7_3

References: T. Takahama and S. Sakai, “Solving difficult constrained optimization problems by the ε constrained differential evolution with gradient based mutation”

in Constraint-Handling in Evolutionary Optimization, E. Mezura-Montes, Ed. Springer-Verlag, 2009, pp. 51–72.

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.EPSDEGData(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 = 0.9, gradient_base_mutation_rate: float = 0.2, tolerance_h: float = 0.001, theta: int = None, penalty_power: int = 2, control_generations: int = 150, gradient_mutation_interval: int = 5, g_funcs: list[typing.Callable[[list[float]], float]] = <factory>, h_funcs: list[typing.Callable[[list[float]], float]] = <factory>)

Bases: BaseData

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

boundary_constraints_fun: BoundaryFixing = 'random'

control_generations: int = 150

crossover_rate: float = 0.9

derivative_method = 'numeric'

dimension: int = 10

function: FitnessFunctionBase = None

g_funcs: list[Callable[[list[float]], float]]

gradient_base_mutation_rate: float = 0.2

gradient_mutation_interval: int = 5

h_funcs: list[Callable[[list[float]], float]]

lb: list

log_population: bool = False

max_nfe: int = 100000

mutation_factor: float = 0.5

optimization_type: OptimizationType = 'minimization'

parallel_processing: list | None = None

penalty_power: int = 2

population_size: int = 100

show_plots: bool = True

theta: int = None

tolerance_h: float = 0.001

ub: list