Extending the Genetic Algorithm

Abstract

The previous chapter provided an overview of the main concepts behind the GA. Since the introduction and popularisation of the GA, a substantial body of research has been undertaken in order to extend the canonical model and to increase the utility of the GA for hard, real-world problems.While it is beyond the scope of any single book to cover all of this work, in this chapter we introduce the reader to a selection of concepts drawn from this research. Many of the ideas introduced in this chapter have general application across the multiple families of natural computing algorithms and are not therefore limited to GAs. The chapter concludes with an introduction to Estimation of Distribution Algorithms (EDAs). EDAs are an alternative way of modelling the learning which is embedded in a population of genotypes in an evolutionary algorithm and have attracted notable research interest in the GA community in recent years.