Created by W.Langdon from gp-bibliography.bib Revision:1.6728
In EA practice one can distinguish two complementary approaches. The first approach uses indirect representations where a solution is encoded in a standard data structure, such as strings, vectors, or discrete permutations, and standard off-the-shelf search operators are applied to these genotypes. This is for example the case in standard genetic algorithms, evolution strategies, and some genetic programming approaches like grammatical evolution or Cartesian genetic programming. To evaluate the solution, the genotype needs to be mapped to the phenotype space. The proper choice of this genotype-phenotype mapping is important for the performance of the EA search process. The second approach, the direct representation, encodes solutions to the problem in its most natural space and designs search operators to operate on this representation.
Research in the last few years has identified a number of key concepts to analyse the influence of representation-operator combinations on EA performance. Relevant properties of representations are locality and redundancy.
Locality is a result of the interplay between the search operator and the genotype-phenotype mapping. 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 favour some kind of phenotypes.
The tutorial gives a brief overview about existing guidelines for representation design, illustrates the 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.",
Genetic Programming entries for Franz Rothlauf