simpleEA: Simple (mu + lambda) EA implementation.
In jakobbossek/ecr: Evolutionary Computing in R

A simple evolutionary (mu + lambda) strategy for the optimization of real-valued functions.

simpleEA(task, n.population = 10L, n.offspring = 10L,
  parent.selector = setupSimpleSelector(), mutator = setupGaussMutator(),
  recombinator = setupCrossoverRecombinator(),
  survival.selector = setupTournamentSelector(2L), max.iter = NULL,
  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 (mu). Default is 10.
`n.offspring`	[`integer(1)`] Number of offspring (lambda) generated in each generation. Default is 10.
`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`.
`survival.selector`	[`ecr_selector`] Selection operator which implements a procedurce to extract individuals from a given set, which should survive and set up the next generation.
`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_single_objective_result]

This helper function hides the regular ecr interface and offers a more R like interface to a simple evolutionary algorithm which works on real valued vectors.

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