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Scilab help >> Genetic Algorithms > Algorithms > 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)



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


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


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


the mutation probability (default value: 0.1).


the crossover probability (default value: 0.7).


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


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).


the radius of the sharing area.


the power coefficient of the penalty formula.


the population of optimal individuals.


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


the initial population of individuals (optional).


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


  • 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
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Last updated:
Mon Oct 01 17:34:52 CEST 2012