Coevolutionary species adaptation genetic algorithms: A continuing SAGA on coupled fitness landscapes

Bull, Larry

Abstract

The Species Adaptation Genetic Algorithm (SAGA) was introduced to facilitate the open-ended evolution of artificial systems. The approach enables genotypes to increase in length through appropriate mutation operators and has been successfully exploited in the production of artificial neural networks in particular. Most recently, this has been undertaken within coevolutionary or multiagent scenarios. This paper uses an abstract model of coevolution to examine the behaviour of SAGA on fitness landscapes which are coupled to those of other evolving entities to varying degrees. Results indicate that the basic dynamics of SAGA remain unchanged but that the rate of genome growth is affected by the degree of coevolutionary interdependence between the entities. © Springer-Verlag Berlin Heidelberg 2005.

• Presentation Conference Type: Conference Paper (published)
• Publication Date: Dec 1, 2005
• Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Print ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Not Peer Reviewed
• Volume: 3630 LNAI
• Pages: 322-331
• Series Title: Lecture Notes in Computer Science
• Series Number: 4648
• DOI: https://doi.org/10.1007/11553090_33
• Keywords: artificial intelligence, computation by abstract devices, user interfaces and human computer interaction, discrete mathematics in computer science, pattern recognition, bioinformatics
• Public URL: https://uwe-repository.worktribe.com/output/1054131
• Publisher URL: http://dx.doi.org/10.1007/11553090_33