Searching for novel clustering programs

Conferences Papers
  1. Enrique Naredo and Leonardo Trujillo. Searching for novel clustering programs. In GECCO. 2013, 1093-1100. BibTeX

    @inproceedings{DBLP:conf/gecco/NaredoT13,
    	author = "Enrique Naredo and Leonardo Trujillo",
    	title = "Searching for novel clustering programs",
    	booktitle = "GECCO",
    	year = 2013,
    	pages = "1093-1100",
    	ee = "http://doi.acm.org/10.1145/2463372.2463505",
    	crossref = "DBLP:conf/gecco/2013",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
  2. Christian Blum and Enrique Alba (eds.). Genetic and Evolutionary Computation Conference, GECCO '13, Amsterdam, The Netherlands, July 6-10, 2013. ACM, 2013. BibTeX

    @proceedings{DBLP:conf/gecco/2013,
    	editor = "Christian Blum and Enrique Alba",
    	title = "Genetic and Evolutionary Computation Conference, GECCO '13, Amsterdam, The Netherlands, July 6-10, 2013",
    	booktitle = "GECCO",
    	publisher = "ACM",
    	year = 2013,
    	isbn = "978-1-4503-1963-8",
    	ee = "http://dl.acm.org/citation.cfm?id=2463372",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
Abstract

Novelty search (NS) is an open-ended evolutionary algorithm that eliminates the need for an explicit objective function. Instead, NS focuses selective pressure on the search for novel solutions. NS has produced intriguing results in specialized domains, but has not been applied in most machine learning areas. The key component of NS is that each individual is described by the behavior it exhibits, and this description is used to determine how novel each individual is with respect to what the search has produced thus far. However, describing individuals in behavioral space is not trivial, and care must be taken to properly define a descriptor for a particular domain. This paper applies NS to a mainstream pattern analysis area: data clustering. To do so, a descriptor of clustering performance is proposed and tested on several problems, and compared with two control methods, Fuzzy C-means and K-means. Results show that NS can effectively be applied to data clustering in some circumstances. NS performance is quite poor on simple or easy problems, achieving basically random performance. Conversely, as the problems get harder NS performs better, and outperforming the control methods. It seems that the search space exploration induced by NS is fully exploited only when generating good solutions is more challenging.

Published in
GECCO '13 Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference
Pages 1093-1100
http://dl.acm.org/citation.cfm?id=2463505
Date of conference
03-05 Abril 2013
ISBN
978-1-4503-1963-8
Last modified onTuesday, 08 October 2013 04:32
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