A common genetic encoding for both direct and indirect encodings of networks

Authors:
Yohannes Kassahun;Mark Edgington;Jan Hendrik Metzen;Gerald Sommer;Frank Kirchner
Affiliations:
University of Bremen, Bremen, Germany;University of Bremen, Bremen, Germany;University of Bremen, Bremen, Germany;Christian Albrechts University, Kiel, Germany;University of Bremen: German Research Center for Artificial Intelligence (DFKI), Bremen, Germany
Venue:
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Year:
2007

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Abstract

In this paper we present a Common Genetic Encoding (CGE) for networks that can be applied to both direct and indirect encoding methods. As a direct encoding method, CGE allows the implicit evaluation of an encoded phenotype without the need to decode the phenotype from the genotype. On the other hand, one can easily decode the structure of a phenotype network, since its topology is implicitly encoded in the genotype's gene-order. Furthermore, we illustrate how CGE can be used for the indirect encoding of networks. CGE has useful properties that makes it suitable for evolving neural networks. A formal definition of the encoding is given, and some of the important properties of the encoding are proven such as its closure under mutation operators, its completeness in representing any phenotype network, and the existence of an algorithm that can evaluate any given phenotype without running into an infinite loop.