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Genome length as an evolutionary self-adaptation

Ramsey, CL and De Jong, KA and Grefenstettc, JJ and Wu, AS and Burke, DS (1998) Genome length as an evolutionary self-adaptation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1498 L. 345 - 353. ISSN 0302-9743

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Abstract

There is increasing interest in evolutionary algorithms that have variahle-length genomes and/or location independent genes. However, our understanding of such algorithms both theoretically and empirically is much less well developed than the more traditional fixed-length, fixed-location ones. Recent studies with VIV (Virtual Virus), a variable length, GA-based computational model of viral evolution, have revealed several emergent phenomena of both biological and computational interest. One interesting and somewhat surprising result is that the length of individuals in the population self-adapts in direct response to the mutation rate applied, so the GA adaptively strikes the balance it needs to successfully solve the problem. Over a broad range of mutation rates, genome length tends to increase dramatically in the early phases of evolution, and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final population) generally increases in response to an increase in the base mutation rate. Furthermore, the mutation operator rate and adapted length resulting in the best problem solving performance is about one mutation per individual. This is also the rate at which mutation generally occurs in biological systems, suggesting an optimal, or at least biologically plausible, balance of these operator rates. These results suggest that an important property of these algorithms is a considerable degree of self-adaptation.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Ramsey, CL
De Jong, KA
Grefenstettc, JJ
Wu, AS
Burke, DSdonburke@pitt.eduDONBURKE
Centers: Other Centers, Institutes, or Units > Center for Vaccine Research
Date: 1 December 1998
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 1498 L
Page Range: 345 - 353
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Epidemiology
Refereed: Yes
ISSN: 0302-9743
Date Deposited: 05 May 2015 16:46
Last Modified: 22 Jun 2019 14:55
URI: http://d-scholarship.pitt.edu/id/eprint/24649

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