Please note that the recommended version of Scilab is 2025.0.0. This page might be outdated.
See the recommended documentation of this function
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
Authors
- Yann COLLETTE
ycollet@freesurf.fr
| << optim_ga | Genetic Algorithms | optim_nsga >> |