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selection_ga_elitist
An 'elitist' selection function
Syntax
[Pop_out,FObj_Pop_out,Efficiency,MO_Total_FObj_out] = selection_ga_elitist(Pop_in,Indiv1,Indiv2,FObj_Pop_in,FObj_Indiv1,FObj_Indiv2,MO_Total_FObj_in,MO_FObj_Indiv1,MO_FObj_Indiv2,param)
Arguments
- Pop_in
The initial population of individuals.
- Indiv1
a first set of children generated via crossover + mutation.
- Indiv2
a second set of children generated via crossover + mutation.
- FObj_Pop_in
a vector of objective function values associated to each individuals of Pop_in.
- FObj_Indiv1
a vector of objective function values associated to each individuals of Indiv1.
- FObj_Indiv2
a vector of objective function values associated to each individuals of Indiv2.
- MO_Total_FObj_in
a matrix of multi-objective function values associated to each individuals of Pop_in.
- MO_FObj_Indiv1
a matrix of multi-objective function values associated to each individuals of Indiv1.
- MO_FObj_Indiv2
a matrix of multi-objective function values associated to each individuals of Indiv2.
- param
a list of parameters. - 'pressure': the selection pressure coefficient. Each individuals with 0 efficiency will have an efficiency value equal to 'pressure'.
- Pop_out
all the selected individuals in a population of size pop_size.
- FObj_Pop_out
all the objective function values associated to each individuals of Pop_out.
- Efficiency
all the efficiency values associated to each individuals of Pop_out.
- MO_Total_FObj_out
all the multi-objective function values associated to each individuals of Pop_out.
Description
This function performs the elitist selection function. We select the best individuals in the set of parents and children individuals.
See also
- selection_ga_random — A function which performs a random selection of individuals
- mutation_ga_default — A continuous variable mutation function
- crossover_ga_default — A crossover function for continuous variable functions
- init_ga_default — A function a initialize a population
- optim_ga — A flexible genetic algorithm
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