Abstract
Memetic algorithms are popular approaches to improve pure evolutionary methods. But were and when in the system the local search should be applied and does it really speed up evolutionary search is a still an open question. In this paper we investigate the influence of the memetic extensions on globally induced regression and model trees. These evolutionary induced trees in contrast to the typical top-down approaches globally search for the best tree structure, tests at internal nodes and models at the leaves. Specialized genetic operators together with local greedy search extensions allow to the efficient tree evolution. Fitness function is based on the Bayesian information criterion and mitigate the over-fitting problem. The proposed method is experimentally validated on synthetical and real-life datasets and preliminary results show that to some extent memetic approach successfully improve evolutionary induction.
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References
Akaike, H.: A New Look at Statistical Model Identification. IEEE Transactions on Automatic Control 19, 716–723 (1974)
Barros, R.C., Basgalupp, M.P., et al.: A Survey of Evolutionary Algorithms for Decision-Tree Induction. IEEE Transactions on Systems, Man, and Cybernetics, Part C (2011) (in print)
Blake, C., Keogh, E., Merz, C.: UCI Repository of Machine Learning Databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Czajkowski, M., Kretowski, M.: Globally Induced Model Trees: An Evolutionary Approach. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 324–333. Springer, Heidelberg (2010)
Czajkowski, M., Kretowski, M.: An Evolutionary Algorithm for Global Induction of Regression Trees with Multivariate Linear Models. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS, vol. 6804, pp. 230–239. Springer, Heidelberg (2011)
Gendreau, M., Potvin, J.Y.: Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 146 (2010)
Kretowski, M.: A Memetic Algorithm for Global Induction of Decision Trees. In: Geffert, V., Karhumäki, J., Bertoni, A., Preneel, B., Návrat, P., Bieliková, M. (eds.) SOFSEM 2008. LNCS, vol. 4910, pp. 531–540. Springer, Heidelberg (2008)
Kretowski, M., Czajkowski, M.: An Evolutionary Algorithm for Global Induction of Regression Trees. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS, vol. 6114, pp. 157–164. Springer, Heidelberg (2010)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)
Rokach, L., Maimon, O.: Top-down induction of decision trees classifiers - A survey. IEEE Transactions on Systems, Man, and Cybernetics - Part C 35(4), 476–487 (2005)
Schwarz, G.: Estimating the Dimension of a Model. The Annals of Statistics 6, 461–464 (1978)
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Czajkowski, M., Kretowski, M. (2012). Does Memetic Approach Improve Global Induction of Regression and Model Trees?. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_20
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DOI: https://doi.org/10.1007/978-3-642-29353-5_20
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