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.
The work described in this paper was done within TSCHECHOW, a research project funded by the basic research funding program of Upper Austria University of Applied Sciences.
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Dorfer, V., Winkler, S.M., Kern, T., Petz, G., Faschang, P. (2012). Analysis of Single-Objective and Multi-Objective Evolutionary Algorithms in Keyword Cluster Optimization. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_52
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DOI: https://doi.org/10.1007/978-3-642-27549-4_52
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