@inproceedings{engelbrecht:2000:SFUDTBBESCD, abstract = {A new evolutionary search algorithm, called BGP (Building-block approach to Genetic Programming), to be used for classification tasks in data mining, is introduced. It is different from existing evolutionary techniques in that it does not use indirect representations of a solution, such as bit strings or grammars. The algorithm uses decision trees of various sizes as individuals in the populations and operators, e.g. crossover, are performed directly on the trees. When compared to the C4.5 and CN2 induction algorithms on a benchmark set of problems, BGP shows very good results}, added-at = {2008-06-19T17:46:40.000+0200}, address = {La Jolla Marriott Hotel La Jolla, California, USA}, author = {Rouwhorst, S. E. and Engelbrecht, A. P.}, biburl = {https://www.bibsonomy.org/bibtex/2dd49611540843e43ce5cf6b93ea9c6f7/brazovayeye}, booktitle = {Proceedings of the 2000 Congress on Evolutionary Computation CEC00}, interhash = {de7286c98ddaef413371f4ba8cf282b0}, intrahash = {dd49611540843e43ce5cf6b93ea9c6f7}, isbn = {0-7803-6375-2}, keywords = {BGP C4.5 CN2 algorithm, algorithms, blocks, building classification classification, computation, data databases, decision evolutionary genetic hybrid induction mathematical mining, operators operators, pattern problems, programming, search systems, trees,}, month = {6-9 July}, notes = {CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644 Inspec Accession Number: 6734684 Comparsion in \cite{yu:2004:ECDM}}, organisation = {IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)}, pages = {633--638}, publisher = {IEEE Press}, publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA}, size = {6 pages}, timestamp = {2008-06-19T17:50:45.000+0200}, title = {Searching the Forest: Using Decision Trees as Building Blocks for Evolutionary Search in Classification Databases}, volume = 1, year = 2000 }