Abstract
In this chapter we investigate the application of diversity-preserving mechanisms in Pitt-style evolutionary classifier systems. Specifically, we analyze the effects of implicit fitness sharing, spatially distributed subpopulations, and combinations of the two, using a range of standard knowledge discovery tasks. The proposed models are compared based on (a) their ability to promote and/or maintain diversity across the evolving population; (b) the ability of the algorithm to evolve rule sets, which accurately classify data; and (c) the relative ease of parallel implementation of the models. Conclusions are drawn regarding the suitability of the approaches in both sequential and parallel environments.
Triantaphyllou, E. and G. Felici (Eds.), Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques, Massive Computing Series, Springer, Heidelberg, Germany, pp. 433–457, 2006.
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Kirley, M., Abbass, H.A., McKay, R.(.I. (2006). Diversity Mechanisms in Pitt-Style Evolutionary Classifier Systems. In: Triantaphyllou, E., Felici, G. (eds) Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques. Massive Computing, vol 6. Springer, Boston, MA . https://doi.org/10.1007/0-387-34296-6_13
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DOI: https://doi.org/10.1007/0-387-34296-6_13
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