Intelligent Exploration for Genetic Algorithms -- Using Self-Organizing Maps in Evolutionary Computation

Fachberichte Informatik

Intelligent Exploration for Genetic Algorithms -- Using Self-Organizing Maps in Evolutionary Computation

Heni Ben Amor and Achim Rettinger. Intelligent Exploration for Genetic Algorithms -- Using Self-Organizing Maps in Evolutionary Computation. Fachberichte Informatik 1--2005, Universität Koblenz-Landau, 2005.

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Abstract

Exploration vs. exploitation is a well known issue in Evolutionary Algorithms. Accordingly, an unbalanced search can lead to premature convergence. GASOM, a novel Genetic Algorithm, addresses this problem by intelligent exploration techniques. The approach uses Self-Organizing Maps to mine data from the evolution process. The information obtained is successfully utilized to enhance the search strategy and confront genetic drift. This way, local optima are avoided and exploratory power is maintained. The evaluation of GASOM on well known problems shows that it effectively prevents premature convergence and seeks the global optimum. Particularly in deceptive and missleading functions it showed outstanding performance. Additionally, representing the search history by the Self-Organizing Map provides a visually pleasing insight into the state and course of evolution.

BibTeX

@TechReport{	  benamor:rettinger:1:2005,
  author	= "Heni Ben Amor and Achim Rettinger",
  title		= "{Intelligent Exploration for Genetic Algorithms -- Using
		  Self-Organizing Maps in Evolutionary Computation}",
  institution	= "{Universit{\"a}t Koblenz-Landau}",
  year		= 2005,
  type		= "Fachberichte Informatik",
  number	= "1--2005",
  language	= "english",
  address	= "Universit{\"a}t Koblenz-Landau, Institut f{\"u}r
		  Informatik, Universit{\"a}tsstr. 1, D-56070 Koblenz",
  url		= "http://www.uni-koblenz.de/fb4/publikationen/gelbereihe/RR-1-2005.pdf"
		  ,
  abstract	= "Exploration vs. exploitation is a well known issue in
		  Evolutionary Algorithms. Accordingly, an unbalanced search
		  can lead to premature convergence. GASOM, a novel Genetic
		  Algorithm, addresses this problem by intelligent
		  exploration techniques. The approach uses Self-Organizing
		  Maps to mine data from the evolution process. The
		  information obtained is successfully utilized to enhance
		  the search strategy and confront genetic drift. This way,
		  local optima are avoided and exploratory power is
		  maintained. The evaluation of GASOM on well known problems
		  shows that it effectively prevents premature convergence
		  and seeks the global optimum. Particularly in deceptive and
		  missleading functions it showed outstanding performance.
		  Additionally, representing the search history by the
		  Self-Organizing Map provides a visually pleasing insight
		  into the state and course of evolution.",
  issn		= "1860-4471"
}