DETCR

DETCR (Hybrid DE Algorithm With Adaptive Crossover Operator)

class DETAlgs.detcr.DETCR(params: DETCRData, db_conn=None, db_auto_write=False)

Bases: BaseAlg

DETCR - Hybrid DE Algorithm With Adaptive Crossover Operator

Links: https://ieeexplore.ieee.org/document/5949800

References: Gilberto Reynoso-Meza; Javier Sanchis; Xavier Blasco; Juan M. Herrero, “Hybrid DE algorithm with adaptive crossover operator for solving real-world numerical optimization problems”, 2011 IEEE Congress of Evolutionary Computation (CEC), New Orleans, LA, USA, 2011, doi: 10.1109/CEC.2011.5949800.

adapting_triangular_distribution(mutation_factories: List)

adaptive_mechanism()

binomial_recombination(org_member: Member, mut_member: Member, cr)

check_optimization_bounds()

crossing(origin_population: Population, mutated_population: Population, cr_list: List, linear_recombination)

lineal_recombination(org_member: Member, mut_member: Member, linear_recombination)

local_search(member: Member, function_eval, optimization: OptimizationType)

mutation(population: Population, f: List)

mutation_ind(base_member: Member, member1: Member, member2: Member, f)

next_epoch()

property nfe: int

Number of function evaluations performed so far.

population_refreshment_mechanism(pop: Population)

run()

selection(origin_population: Population, modified_population: Population)

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.DETCRData(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, number_of_success_crossover_rate: int = 15, lineal_recombination_factor: float = 0.75, gamma_var: int = 3)

Bases: BaseData

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

boundary_constraints_fun: BoundaryFixing = 'random'

dimension: int = 10

function: FitnessFunctionBase = None

gamma_var: int = 3

lb: list

lineal_recombination_factor: float = 0.75

log_population: bool = False

max_nfe: int = 100000

number_of_success_crossover_rate: int = 15

optimization_type: OptimizationType = 'minimization'

parallel_processing: list | None = None

population_size: int = 100

show_plots: bool = True

triangular_distribution_for_crossover_rate = [0.2, 0.5, 1.0]

triangular_distribution_for_mutation_factory = [0.3, 0.4, 0.5]

ub: list