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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
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