Authors:
Seamus Hill
and
Colm O’Riordan
Affiliation:
National University of Ireland Galway, Ireland
Keyword(s):
Genetic Algorithms, Neutrality, Operators, Mutation, Diversity.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Representation Techniques
;
Soft Computing
Abstract:
By adopting a basic interpretation of the biological processes of transcription and translation, the multilayered
GA (MGA) introduces a genotype-phenotype mapping for a haploid genotype, which allows the granularity
of the representation to be tuned. The paper examines the impact of altering the level of neutrality
through changes in the granularity of the representation and compares the performance of a standard GA
(SGA) to that of a number of multi-layered GAs, each with a different level of neutrality, over both static and
changing environments. Initial results indicate that it appears advantageous to include a multi-layered, biologically
motivated genotype-phenotype encoding over more difficult landscapes. The paper also introduces an
interpretation of missense mutation, which operates within the genotype-phenotype map (GP-map). Results
also suggest that this mutation strategy can assist in tracking the optimum over various landscapes.