onePlusOneGA: Simple (1 + 1) Genetic Algorithm.
In jakobbossek/ecr: Evolutionary Computing in R

Description Usage Arguments Value Note

The simplest evolutionary algorithm one can imagine, namely the (1+1) EA/GA. Maintains a population of a single individual x and uses just bitplip mutation to generate a child y (obviously no recombination takes place), i.e., each gene of x is flipped with probability p independently. The best individual survives. This algorithm is of particular interest in the theory of evolutionary algorithms and its performance is well understood for different function families. A lot of interesting results exist.

1 2	onePlusOneGA(task, p = NULL, 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.
`p`	[`numeric(1)`] Mutation probability for bitplip mutation. Default is 1/n where n is the length of the gene.
`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 binary valued vectors.

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