On Measuring the Attributes of Evolutionary Algorithms: A Comparison of Algorithms Used for Information Retrieval
J. L. Fernandez-Villacanas Martin (Universidad Carlos III, Spain), P. Marrow (Intelligent Systems Laboratory, Btextract Technologies, UK) and M. Shackleton (Intelligent Systems Laboratory, Btextract Technologies, UK)
Copyright: © 2008
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.