Methods for the edaOptimize generic function.
edaOptimizeDisabled(eda, gen, pop, popEval, f, lower, upper)Generation.
Matrix with one row for each solution in the population.
Vector with the evaluation of each solution in pop.
Objective function.
Lower bounds of the variables of the objective function.
Upper bounds of the variables of the objective function.
A list with the following components.
Matrix with one row for each solution in the optimized population.
Vector with the evaluation of each solution in pop.
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.
edaOptimizeDisabledDisable local optimization. This is
the default method of the edaOptimize generic function.
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/.