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Analysis of Single-Objective and Multi-Objective Evolutionary Algorithms in Keyword Cluster Optimization

  • Viktoria Dorfer
  • Stephan M. Winkler
  • Thomas Kern
  • Gerald Petz
  • Patrizia Faschang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6927)

Abstract

As it is not trivial to cope with the fast growing number of papers published in the field of medicine and biology, intelligent search strategies are needed to be able to access the required information as fast and accurately as possible. In [5] we have proposed a method for keyword clustering as a first step towards an intelligent search strategy in biomedical information retrieval. In this paper we focus on the analysis of the internal dynamics of the evolutionary algorithms applied here using solution encoding specific population diversity analysis, which is also defined in this paper. The population diversity results obtained using evolution strategies, genetic algorithms, genetic algorithms with offspring selection and also a multi-objective approach, the NSGA-II, are discussed here. We see that the diversity of the populations is preserved over the generations, decreasing towards the end of the runs, which indicates a good performance of the selection process.

Keywords

Genetic Algorithm Evolutionary Algorithm Solution Candidate Keyword Cluster Query Extension 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Viktoria Dorfer
    • 1
  • Stephan M. Winkler
    • 1
  • Thomas Kern
    • 1
  • Gerald Petz
    • 2
  • Patrizia Faschang
    • 2
  1. 1.School of Informatics, Communications and MediaUpper Austria University of Applied SciencesHagenbergAustria
  2. 2.School of ManagementUpper Austria University of Applied SciencesSteyrAustria

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