edaOptimize: Local Optimization Methods

In yasserglez/copulaedas: Estimation of Distribution Algorithms Based on Copulas

Description

Methods for the edaOptimize generic function.

Usage

edaOptimizeDisabled(eda, gen, pop, popEval, f, lower, upper)

Arguments

eda	EDA instance.
gen	Generation.
pop	Matrix with one row for each solution in the population.
popEval	Vector with the evaluation of each solution in pop.
f	Objective function.
lower	Lower bounds of the variables of the objective function.
upper	Upper bounds of the variables of the objective function.

Details

Local optimization methods improve the solutions sampled by the search distribution. These methods can also be used to implement repairing strategies for constrained problems in which the simulated solutions may be unfeasible and some strategy to repair these solutions is available.

The following local optimization methods are implemented.

edaOptimizeDisabled

Disable local optimization. This is the default method of the edaOptimize generic function.

Value

A list with the following components.

pop	Matrix with one row for each solution in the optimized population.
popEval	Vector with the evaluation of each solution in pop.

References

Gonzalez-Fernandez Y, Soto M (2014). copulaedas: An R Package for Estimation of Distribution Algorithms Based on Copulas. Journal of Statistical Software, 58(9), 1-34. http://www.jstatsoft.org/v58/i09/.

yasserglez/copulaedas documentation built on June 9, 2021, 10:05 a.m.