rmoo_Crossover: Crossover operators in non-dominated genetic algorithms

In Evolutionary-Optimization-Laboratory/rmoo: Multi-Objective Optimization in R

Crossover operators in non-dominated genetic algorithms

Description

Functions implementing crossover non-dominated genetic operator.

Usage

  rmoo_spCrossover(object, parents)

  rmoobin_spCrossover(object, parents)

  rmooreal_spCrossover(object, parents)
  rmooreal_sbxCrossover(object, parents, eta = 20, indpb = 0.5)

  rmooperm_oxCrossover(object, parents)

Arguments

object	An object of class "nsga", "nsga2" and "nsga3", usually resulting from a call to function nsga, nsga2 and nsga3.
parents	A two-rows matrix of values indexing the parents from the current population.
eta	Distribution parameter for crossover operator.
indpb	Independent crossover probability.

Value

Return a list with two elements:

children	a matrix of dimension 2 times the number of decision variables containing the generated offsprings;
fitness	a vector of length 2 containing the fitness values for the offsprings. A value NA is returned if an offspring is different (which is usually the case) from the two parents.

Author(s)

Francisco Benitez

References

Scrucca, L. (2017) On some extensions to 'GA' package: hybrid optimisation, parallelisation and islands evolution. The R Journal, 9/1, 187-206, doi: 10.32614/RJ-2017-008.

See Also

nsga(), nsga2() and nsga3()

Evolutionary-Optimization-Laboratory/rmoo documentation built on Oct. 28, 2024, 5:45 p.m.