Representation and structural difficulty in genetic programming

@article{citeulike:2374194, abstract = {Standard tree-based genetic programming suffers from a structural difficulty problem in that it is unable to search effectively for solutions requiring very full or very narrow trees. This deficiency has been variously explained as a consequence of restrictions imposed by the tree structure or as a result of the numerical distribution of tree shapes. We show that by using a different tree-based representation and local (insertion and deletion) structural modification operators, that this problem can be almost eliminated even with trivial (stochastic hill-climbing) search methods, thus eliminating the above explanations. We argue, instead, that structural difficulty is a consequence of the large step size of the operators in standard genetic programming, which is itself a consequence of the fixed-arity property embodied in its representation.}, author = {Hoai, Nguyen X. and Mckay, R. I. and Essam, D. }, booktitle = {Evolutionary Computation, IEEE Transactions on}, citeulike-article-id = {2374194}, doi = {10.1109/TEVC.2006.871252}, journal = {Evolutionary Computation, IEEE Transactions on}, keywords = {evolution, evolutionary-computation, genetic-programming, grammar, modularity, representation, structures, tag}, number = {2}, pages = {157--166}, posted-at = {2008-02-14 14:24:31}, priority = {3}, title = {Representation and structural difficulty in genetic programming}, url = {http://dx.doi.org/10.1109/TEVC.2006.871252}, volume = {10}, year = {2006} }

TY - JOUR ID - citeulike:2374194 L3 - citeulike-article-id:2374194 TI - Representation and structural difficulty in genetic programming T2 - Evolutionary Computation, IEEE Transactions on JF - Evolutionary Computation, IEEE Transactions on VL - 10 IS - 2 SP - 157 EP - 166 N2 - Standard tree-based genetic programming suffers from a structural difficulty problem in that it is unable to search effectively for solutions requiring very full or very narrow trees. This deficiency has been variously explained as a consequence of restrictions imposed by the tree structure or as a result of the numerical distribution of tree shapes. We show that by using a different tree-based representation and local (insertion and deletion) structural modification operators, that this problem can be almost eliminated even with trivial (stochastic hill-climbing) search methods, thus eliminating the above explanations. We argue, instead, that structural difficulty is a consequence of the large step size of the operators in standard genetic programming, which is itself a consequence of the fixed-arity property embodied in its representation. KW - evolution KW - evolutionary-computation KW - genetic-programming KW - grammar KW - modularity KW - representation KW - structures KW - tag AU - Hoai, Nguyen AU - Mckay, RI AU - Essam, D PY - 2006/// UR - http://dx.doi.org/10.1109/TEVC.2006.871252 ER -