Representations for Evolutionary Algorithms
Description
Successful and efficient use of evolutionary algorithms depends on the choice of the genotype, the problem representation (mapping from genotype to phenotype) and on the choice of search operators that are applied to the genotypes. These choices cannot be made independently of each other. The question whether a certain representation leads to better performing EAs than an alternative representation can only be answered when the operators applied are taken into consideration. The reverse is also true: deciding between alternative operators is only meaningful for a given representation.
Research in the last few years has identified a number of key concepts to analyse the influence of representation-operator combinations on the performance of evolutionary algorithms. Relevant concepts are the locality and redundancy of representations. Locality is a result of the interplay between the search operator and the genotype-phenotype mapping. Representations have high locality if the application of variation operators results in new solutions similar to the original ones. Representations are redundant if the number of phenotypes exceeds the number of possible genotypes. Redundant representations can lead to biased encodings if some phenotypes are on average represented by a larger number of genotypes or search operators favor some kind of phenotypes.
The tutorial gives an overview about existing guidelines for representation design, illustrates different aspects of representations, gives a brief overview of models describing the different aspects, and illustrates the relevance of the aspects with practical examples.
It is expected that the participants have a basic understanding of EA principles.
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