Description Usage Arguments Details Value References
Methods for the edaOptimize
generic function.
1  edaOptimizeDisabled(eda, gen, pop, popEval, f, lower, upper)

eda 

gen 
Generation. 
pop 
Matrix with one row for each solution in the population. 
popEval 
Vector with the evaluation of each solution in 
f 
Objective function. 
lower 
Lower bounds of the variables of the objective function. 
upper 
Upper bounds of the variables of the objective function. 
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.
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 
GonzalezFernandez Y, Soto M (2014). copulaedas: An R Package for Estimation of Distribution Algorithms Based on Copulas. Journal of Statistical Software, 58(9), 134. http://www.jstatsoft.org/v58/i09/.
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