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Putting more genetics into genetic algorithms.

Burke, DS and De Jong, KA and Grefenstette, JJ and Ramsey, CL and Wu, AS (1998) Putting more genetics into genetic algorithms. Evolutionary computation, 6 (4). 387 - 410. ISSN 1063-6560

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Abstract

The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). VIV incorporates a number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can vary in length and may contain noncoding regions, as well as duplicative or competing genes. Initial computational studies with VIV have already revealed several emergent phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, 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. When VIV converges, there tend to be many copies of good alternative genes within the individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion that noncoding regions serve as scratch space in which VIV can explore alternative gene values. These results represent a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing better tools for understanding evolving biological systems.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Burke, DSdonburke@pitt.eduDONBURKE
De Jong, KA
Grefenstette, JJgref@pitt.eduGREF
Ramsey, CL
Wu, AS
Centers: Other Centers, Institutes, or Units > Center for Vaccine Research
Date: 1 January 1998
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Evolutionary computation
Volume: 6
Number: 4
Page Range: 387 - 410
DOI or Unique Handle: 10.1162/evco.1998.6.4.387
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Epidemiology
Refereed: Yes
ISSN: 1063-6560
Date Deposited: 07 May 2015 20:42
Last Modified: 02 Feb 2019 16:57
URI: http://d-scholarship.pitt.edu/id/eprint/24344

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