rmoo_Crossover: Crossover Operators in Non-Dominated Genetic Algorithms

In rmoo: Multi-Objective Optimization in R

Crossover Operators in Non-Dominated Genetic Algorithms

Description

Functions implementing crossover operators for non-dominated genetic algorithms. rmoo_spCrossover (and its typed variants rmoobin_, rmooreal_, rmooint_) performs single-point crossover; rmooreal_sbxCrossover performs simulated binary crossover; rmooperm_oxCrossover performs order crossover for permutation representations.

Usage

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

rmoo_spCrossover(object, parents)

rmoobin_spCrossover(object, parents)

rmooreal_spCrossover(object, parents)

rmooint_spCrossover(object, parents)

rmooperm_oxCrossover(object, parents)

Arguments

object	An object of class "nsga", "nsga2", or "nsga3", usually from a call to nsga, nsga2, or nsga3.
parents	A two-element integer vector indexing the parents from the current population.
eta	The distribution index. A higher eta produces offspring closer to the parents, while a lower eta allows for larger differences.
indpb	The probability of a particular gene being crossed.

Value

A list with two elements:

children

A matrix of dimension 2 × nVars containing the generated offspring.

fitness

A 2 × nObj matrix of NA values, indicating that offspring fitness has not yet been evaluated.

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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/RJ-2017-008")}.

See Also

nsga(), nsga2(), nsga3()

rmoo documentation built on May 4, 2026, 1:06 a.m.