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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
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