Description Usage Arguments Details Value References
Methods for the edaSelect
generic function.
1 2 | edaSelectTruncation(eda, gen, pop, popEval)
edaSelectTournament(eda, gen, pop, popEval)
|
eda |
|
gen |
Generation. |
pop |
Matrix with one row for each solution in the population. |
popEval |
Vector with the evaluation of each solution in |
Selection methods determine the solutions to be modeled by the search distribution (selected population). These solutions are usually the most promising solutions of the population. The following selection methods are implemented.
edaSelectTruncation
In truncation selection, the
100 * truncFactor
percent of the solutions with the best evaluation
in the population are selected. The parameter truncFactor
specifies
the truncation factor (default value: 0.3
). This is the default
method of the edaSelect
generic function.
edaSelectTournament
In tournament selection, a group of
solutions are randomly picked from the population and the best one is
selected. This process is repeated as many times as needed to complete
the selected population. The parameter tournamentSize
specifies
the number of solutions randomly picked from the population (default
value: 2
), selectionSize
specifies the size of the selected
population (default value: nrow(pop)
), and replacement
specifies whether to sample with replacement or not (default value:
TRUE
).
An integer
vector with the indexes of the solutions selected
from pop
.
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/.
Pelikan M (2005). Hierarchical Bayesian Optimization Algorithm. Toward a New Generation of Evolutionary Algorithms. Springer-Verlag.
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