Peak analysis for characterizing evolutionary behavior

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Abstract
  • Evolutionary Algorithms (EAs) use a simplified
    abstraction of biological evolution to interact with a fitness landscape
    over multiple generations. The traditional approach to
    exploring evolutionary motivated search relies on performance
    comparisons of competing designs for a common problem domain.
    This approach has been useful in developing an intuition
    of when one mechanism is superior to another. However, this
    intuition has developed in the absence of a clear understanding
    of how fitness landscape topology impacts the search process in
    high-dimensional space, due to the lack of high-dimensional
    visualization tools. Proposed is a behavior measure derived
    from a known set of all problem domain optima, which is used
    as system of landmarks. Using these landmarks, evolutionary
    system progress can be tracked in the problem domain and
    characterized with an information theoretic metric. Thus, this
    technique provides an intuition that takes into account the
    problem domain topology, allowing the behavior of different
    algorithm configurations to be compared as they interact with
    a given topology.
TitlePeak analysis for characterizing evolutionary behavior
CreatorPfaffmann, Jeffrey O.
Publisher2009 IEEE Symposium on Artificial Life
Academic DepartmentComputer Science
DivisionNatural Sciences
OrganizationLafayette College
Date Issued2009
Date Available2010-02-26T19:13:53Z
TypeArticle
LanguageEnglish
Bibliographic CitationPfaffmann, J. O. (2009.) "Peak analysis for characterizing evolutionary behavior." 2009 IEEE Symposium on Artificial Life 155-162.
Standard IdentifierHandle 10385/625
DOI 10.1109/ALIFE.2009.4937707
Permalinkhttp://hdl.handle.net/10385/625
Rights Statement In Copyright In Copyright
Rights HoldersIEEE
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