The Hierarchical Fair Competition Framework for Sustainable Evolutionary Algorithms

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@Article{hu:2005:EC,
  author =       "Jianjun Hu and Erik Goodman and Kisung Seo and 
                 Zhun Fan and Rondal Rosenberg",
  title =        "The Hierarchical Fair Competition Framework for
                 Sustainable Evolutionary Algorithms",
  journal =      "Evolutionary Computation",
  year =         "2005",
  volume =       "13",
  number =       "2",
  pages =        "241--277",
  month =        "Summer",
  keywords =     "genetic algorithms, genetic programming, sustainable
                 evolutionary algorithms, building blocks, premature
                 convergence, diversity, fair competition, hierarchical
                 problem solving",
  ISSN =         "1063-6560",
  publisher =    "MIT Press",
  broken =       "http://www.ingentaconnect.com/content/mitpress/evco/2005/00000013/00000002/art00005",
  DOI =          "doi:10.1162/1063656054088530",
  size =         "37 pages",
  abstract =     "Many current Evolutionary Algorithms (EAs) suffer from
                 a tendency to converge prematurely or stagnate without
                 progress for complex problems. This may be due to the
                 loss of or failure to discover certain valuable genetic
                 material or the loss of the capability to discover new
                 genetic material before convergence has limited the
                 algorithm's ability to search widely. In this paper,
                 the Hierarchical Fair Competition (HFC) model,
                 including several variants, is proposed as a generic
                 framework for sustainable evolutionary search by
                 transforming the convergent nature of the current EA
                 framework into a non-convergent search process. That
                 is, the structure of HFC does not allow the convergence
                 of the population to the vicinity of any set of optimal
                 or locally optimal solutions. The sustainable search
                 capability of HFC is achieved by ensuring a continuous
                 supply and the incorporation of genetic material in a
                 hierarchical manner, and by culturing and maintaining,
                 but continually renewing, populations of individuals of
                 intermediate fitness levels. HFC employs an
                 assembly-line structure in which subpopulations are
                 hierarchically organised into different fitness levels,
                 reducing the selection pressure within each
                 subpopulation while maintaining the global selection
                 pressure to help ensure the exploitation of the good
                 genetic material found. Three EAs based on the HFC
                 principle are tested - two on the even-10-parity
                 genetic programming benchmark problem and a real-world
                 analog circuit synthesis problem, and another on the
                 HIFF genetic algorithm (GA) benchmark problem. The
                 significant gain in robustness, scalability and
                 efficiency by HFC, with little additional computing
                 effort, and its tolerance of small population sizes,
                 demonstrates its effectiveness on these problems and
                 shows promise of its potential for improving other
                 existing EAs for difficult problems. A paradigm shift
                 from that of most EAs is proposed: rather than trying
                 to escape from local optima or delay convergence at a
                 local optimum, HFC allows the emergence of new optima
                 continually in a bottom-up manner, maintaining low
                 local selection pressure at all fitness levels, while
                 fostering exploitation of high-fitness individuals
                 through promotion to higher levels.",
  notes =        "http://mitpress.mit.edu/catalog/item/default.asp?ttype=4&tid=25",
}

Genetic Programming entries for Jianjun Hu Erik Goodman Kisung Seo Zhun Fan Rondal Rosenberg

Citations