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On Measuring the Attributes of Evolutionary Algorithms: A Comparison of Algorithms Used for Information Retrieval
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Author(s): J.-L. Fernandez-Villacanas Martin (Universidad Carlos III, Spain), P. Marrow (Btextract Technologies, UK)and M. Shackleton (Btextract Technologies, UK)
Copyright: 2003
Pages: 22
Source title:
Computational Intelligence in Control
Source Author(s)/Editor(s): Masoud Mohammadian (University of Canberra, Australia), Rahul A. Sarker (University of New South Wales, Australia)and Xin Yao (The University of Birmingham, UK)
DOI: 10.4018/978-1-59140-037-0.ch016
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Abstract
In this chapter we compare the performance of two contrasting evolutionary algorithms addressing a similar problem, of information retrieval. The first, BTGP, is based upon genetic programming, while the second, MGA, is a genetic algorithm. We analyze the performance of these evolutionary algorithms through aspects of the evolutionary process they undergo while filtering information. We measure aspects of the variation existing in the population undergoing evolution, as well as properties of the selection process. We also measure properties of the adaptive landscape in each algorithm, and quantify the importance of neutral evolution for each algorithm. We choose measures of these properties because they appear generally important in evolution. Our results indicate why each algorithm is effective at information retrieval, however they do not provide a means of quantifying the relative effectiveness of each algorithm. We attribute this difficulty to the lack of appropriate measures available to measure properties of evolutionary algorithms, and suggest some criteria for useful evolutionary measures to be developed in the future.
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