optim_nsga2

Arguments

ga_f

the function to be optimized. The prototype if y = f(x) or y = list(f,p1,p2,...).

pop_size

the size of the population of individuals (default value: 100).

nb_generation

the number of generations (equivalent to the number of iterations in classical optimization) to be computed (default value: 10).

p_mut

the mutation probability (default value: 0.1).

p_cross

the crossover probability (default value: 0.7).

Log

if %T, we will display to information message during the run of the genetic algorithm.

param

a list of parameters.

• 'codage_func': the function which will perform the coding and decoding of individuals (default function: codage_identity).

• 'init_func': the function which will perform the initialization of the population (default function: init_ga_default).

• 'crossover_func': the function which will perform the crossover between two individuals (default function: crossover_ga_default).

• 'mutation_func': the function which will perform the mutation of one individual (default function: mutation_ga_default).

• 'selection_func': the function whcih will perform the selection of individuals at the end of a generation (default function: selection_ga_elitist).

• 'nb_couples': the number of couples which will be selected so as to perform the crossover and mutation (default value: 100).

• 'pressure': the value the efficiency of the worst individual (default value: 0.05).

pop_opt

the population of optimal individuals.

fobj_pop_opt

the set of objective function values associated to pop_opt (optional).

pop_init

the initial population of individuals (optional).

fobj_pop_init

the set of objective function values associated to pop_init (optional).

Description

• This function implements the classical "Niched Sharing Genetic Algorithm". For a demonstration, see SCI/modules/genetic_algorithms/examples/NSGA2demo.sce.

See Also

• optim_moga — multi-objective genetic algorithm
• optim_ga — A flexible genetic algorithm
• optim_nsga — A multi-objective Niched Sharing Genetic Algorithm