optim_nsga

A multi-objective Niched Sharing Genetic Algorithm

Calling Sequence

[pop_opt,fobj_pop_opt,pop_init,fobj_pop_init] = optim_nsga(ga_f,pop_size,nb_generation,p_mut,p_cross,Log,param,sigma,pow)

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).

: :sigma the radius of the sharing area. : :pow the power coefficient of the penalty formula. : :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/NSGAdemo.sce.

See Also

• optim_moga multi-objective genetic algorithm
• optim_ga A flexible genetic algorithm
• optim_nsga2 A multi-objective Niched Sharing Genetic Algorithm version 2