Abstract:
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Optimization is concerned with the finding of global optima
(hence the name) of problems that can be cast in the form of a
function of several variables and constraints thereof. Among the
searching methods, {em Evolutionary Algorithms} have been shown to be
adaptable and general tools that have often outperformed traditional
{em ad hoc} methods. The {em Breeder Genetic Algorithm} (BGA)
combines a direct representation with a nice conceptual
simplicity. This work contains a general description of the algorithm
and a detailed study on a collection of function optimization
tasks. The results show that the BGA is a powerful and reliable
searching algorithm. The main discussion concerns the choice of
genetic operators and their parameters, among which the family of
Extended Intermediate Recombination (EIR) is shown to stand out. In
addition, a simple method to dynamically adjust the operator is
outlined and found to greatly improve on the already excellent overall
performance of the algorithm. |