Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A memetic algorithm for global induction of decision trees
SOFSEM'08 Proceedings of the 34th conference on Current trends in theory and practice of computer science
An evolutionary algorithm for global induction of regression trees
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Globally induced model trees: an evolutionary approach
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Handbook of Metaheuristics
An evolutionary algorithm for global induction of regression trees with multivariate linear models
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Top-down induction of decision trees classifiers - a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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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.