nsga2: Implementation of the NSGA-II EMOA algorithm by Deb.
In jakobbossek/ecr: Evolutionary Computing in R

Description Usage Arguments Value Note References

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.

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, ...)

`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`.

[ecr_nsga2_result, ecr_multi_objective_result]

This is a pure R implementation of the NSGA-II algorithm. It hides the regular ecr interface and offers a more R like interface while still being quite adaptable.

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.

jakobbossek/ecr documentation built on May 18, 2019, 9:09 a.m.