A continuous variable mutation function (original) (raw)
Scilab 5.3.3
- Scilab help
- Genetic Algorithms
- coding_ga_binary
- coding_ga_identity
- crossover_ga_binary
- crossover_ga_default
- init_ga_default
- mutation_ga_binary
- mutation_ga_default
- optim_ga
- optim_moga
- optim_nsga
- optim_nsga2
- pareto_filter
- selection_ga_elitist
- selection_ga_random
Please note that the recommended version of Scilab is 2026.0.1. This page might be outdated.
See the recommended documentation of this function
Scilab help >> Genetic Algorithms > mutation_ga_default
mutation_ga_default
A continuous variable mutation function
Calling Sequence
Mut_Indiv = mutation_ga_default(Indiv,param)
Arguments
Indiv
The individual to be mutated.
param
a list of parameters.
- 'delta': a random perturbation will be sampled via an uniform distribution between -delta and + delta.
- 'minbound': a vector of minimum bound for the variable X.
- 'maxbound': a vector of maximum bound for the variable X.
Mut_Indiv
The resulting mutated individual.
Description
- This function performs the classical continuous variable mutation function.
See Also
- mutation_ga_binary — A function which performs binary mutation
- 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
Authors
Yann COLLETTE