Methods for Improving the Design and Performance of Evolutionary Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Bassett:thesis,
  author =       "Jeffrey Kermes Bassett",
  title =        "Methods for Improving the Design and Performance of
                 Evolutionary Algorithms",
  school =       "The Volgenau School of Engineering, George Mason
                 University",
  year =         "2012",
  address =      "USA",
  month =        "Fall",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://hdl.handle.net/1920/8122",
  URL =          "http://digilib.gmu.edu/dspace/bitstream/handle/1920/8122/Bassett_gmu_0883E_10215.pdf",
  URL =          "http://phdtree.org/pdf/23455096-methods-for-improving-the-design-and-performance-of-evolutionary-algorithms/",
  size =         "160 pages",
  abstract =     "Evolutionary Algorithms (EAs) can be applied to almost
                 any optimization or learning problem by making some
                 simple customizations to the underlying representation
                 and/or reproductive operators. This makes them an
                 appealing choice when facing a new or unusual problem.
                 Unfortunately, while making these changes is often
                 easy, getting a customized EA to operate effectively
                 (i.e. find a good solution quickly) can be much more
                 difficult.

                 Ideally one would hope that theory would provide some
                 guidance here, but in these cases, evolutionary
                 computation (EC) theories are not easily applied. They
                 either require customization themselves, or they
                 require information about the problem that essentially
                 includes the solution. Consequently most practitioners
                 rely on an ad-hoc approach, incrementally modifying and
                 testing various customizations until they find
                 something that works reasonably well.

                 The drawback that most EC theories face is that they
                 are closely associated with the underlying
                 representation of an individual (i.e. the genetic
                 code). There has been some success at addressing this
                 limitation by applying a biology theory called
                 quantitative genetics to EAs. This approach allows one
                 to monitor the behaviour of an EA by observing
                 distributions of an outwardly observable phenotypic
                 trait (usually fitness), and thus avoid modelling the
                 algorithm's internal details. Unfortunately, observing
                 a single trait does not provide enough information to
                 diagnose most problems within an EA. It is my
                 hypothesis that using multiple traits will allow one to
                 observe how the population is traversing the search
                 space, thus making more detailed diagnosis possible.

                 In this work, I adapt a newer multivariate form of
                 quantitative genetics theory for use with evolutionary
                 algorithms and derive a general equation of population
                 variance dynamics. This provides a foundation for
                 building a set of tools that can measure and visualize
                 important characteristics of an algorithm, such as
                 exploration, exploitation, and heritability, throughout
                 an EA run. Finally I demonstrate that the tools can
                 actually be used to identify and fix problems in two
                 well known EA variants: Pittsburgh approach rule
                 systems and genetic programming trees.",
  notes =        "Supervisor Kenneth A. De Jong",
}

Genetic Programming entries for Jeffrey K Bassett

Citations