Putting More Genetics into Genetic Algorithms   [GA]

by

Burke, D., S., Jong, K., A., D., Grefenstette, J., J., Ramsey, C., L. and Wu, A., S.

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Info: Evolutionary Computation (Journal), 1998, p. 387-410
Keywords:genetic algorithms, Models of viral evolution, variable-length representation, length penalty functions, genome length adaptation, noncoding regions, duplicative genes
Abstract:
The majority of current genetic algorithms (GAs), [GA] [GAG] 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 [EC] 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). The 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, [NR] 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. [MR] 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. [MR] 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 [NR] serve 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.
Notes:
Special Issue: Variable-Length Representation and Noncoding Segments for Evolutionary Algorithms Edited by Annie S. Wu and Wolfgang Banzhaf
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BibTex:
@Article{burk:1998:pmgGA,
  author =       "Donald S. Burke and Kenneth A. De Jong and John J.
                 Grefenstette and Connie Loggia Ramsey and Annie S. Wu",
  title =        "Putting More Genetics into Genetic Algorithms",
  journal =      "Evolutionary Computation",
  year =         "1998",
  volume =       "6",
  number =       "4",
  pages =        "387--410",
  month =        "Winter",
  keywords =     "genetic algorithms, Models of viral evolution,
                 variable-length representation, length penalty
                 functions, genome length adaptation, noncoding regions,
                 duplicative genes",
  URL =          "http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=10636560&volume=6&issue=4",
  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). The 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 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.",
  notes =        "Special Issue: Variable-Length Representation and
                 Noncoding Segments for Evolutionary Algorithms Edited
                 by Annie S. Wu and Wolfgang Banzhaf",
}