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Aide Scilab >> Algorithmes génétiques > optim_moga

optim_moga

multi-objective genetic algorithm

Calling Sequence

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

Arguments

ga_f

the function to be optimized. The header of the function is the following :

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 multi-objective function values associated to pop_opt (optional).

pop_init

the initial population of individuals (optional).

fobj_pop_init

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

Description

  • This function implements the classical "Multi-Objective Genetic Algorithm". For a demonstration: see SCI/modules/genetic_algorithms/examples/MOGAdemo.sce.

See Also

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

Authors

Yann COLLETTE

ycollet@freesurf.fr

Scilab Enterprises
Copyright (c) 2011-2017 (Scilab Enterprises)
Copyright (c) 1989-2012 (INRIA)
Copyright (c) 1989-2007 (ENPC)
with contributors
Last updated:
Wed Oct 05 12:11:00 CEST 2011