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 .. _optim_ga: optim_ga.html .. _optim_nsga2: optim_nsga2.html .. _optim_moga: optim_moga.html