nsga2: Implementation of the NSGA-II EMOA algorithm by Deb.

Description

The NSGA-II merges the current population and the generated offspring and reduces it by means of the following procedure: It first applies the non dominated sorting algorithm to obtain the nondominated fronts. Starting with the first front, it fills the new population until the i-th front does not fit. It then applies the secondary crowding distance criterion to select the missing individuals from the i-th front.

Usage

nsga2(task, n.population = 100L, n.offspring = n.population, parent.selector = setupSimpleSelector(), mutator = setupGaussMutator(), recombinator = setupCrossoverRecombinator(), max.iter = 100L, max.evals = NULL, max.time = NULL, ...)

Arguments

task

[ecr_optimization_task] Optimization task. If a smoof_function is passed it is automatically converted into a task.

n.population

[integer(1)] Population size. Default is 100.

n.offspring

[integer(1)] Offspring size, i.e., number of individuals generated by variation operators in each iteration. Default is n.population.

parent.selector

[ecr_selector] Selection operator which implements a procedure to copy individuals from a given population to the mating pool, i. e., allow them to become parents.

mutator

[ecr_mutator] Mutation operator of type ecr_mutator.

recombinator

[ecr_recombinator] Recombination operator of type ecr_recombinator.

max.iter

[integer(1)] Maximal number of iterations. Default ist 100L.

max.evals

[integer(1)] Maximal number of iterations/generations. Default is Inf.

max.time

[integer(1)] Time budget in seconds. Default ist Inf.

...

[any] Further arguments passed to setupECRControl.

Value

[ecr_nsga2_result, ecr_multi_objective_result]

References

Deb, K., Pratap, A., and Agarwal, S. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6 (8) (2002), 182-197.