Genetic Algorithms and the Design of Experiments

Abstract

The genetic algorithm (GA) has most often been viewed from a biological perspective. The metaphors of natural selection, cross-breeding and mutation have been helpful in providing a framework in which to explain how and why they work. However, most practical applications of GAs are in the context of optimization, where alternative approaches may prove more effective. In attempting to understand how GAs function as optimizers, several alternative viewpoints have been suggested.