%0 Conference Paper %1 berstein:2004:mpefsgp %A Bernstein, Yaniv %A Li, Xiaodong %A Ciesielski, Vic %A Song, Andy %B Proceedings of the 2004 IEEE Congress on Evolutionary Computation %C Portland, Oregon %D 2004 %I IEEE Press %K Combinatorial Multiobjective \& algorithms, evolutionary genetic numerical optimization programming, %P 83--89 %T Multiobjective Parsimony Enforcement for Superior Generalisation Performance %U http://goanna.cs.rmit.edu.au/~ybernste/papers/Bernstein_CEC_2004.pdf %X Program Bloat - the phenomenon of ever-increasing program size during a GP run - is a recognised and widespread problem. Traditional techniques to combat program bloat are program size limitations or parsimony pressure (penalty functions). These techniques suffer from a number of problems, in particular their reliance on parameters whose optimal values it is difficult to a priori determine. In this paper we introduce POPE-GP, a system that makes use of the NSGA-II multiobjective evolutionary algorithm as an alternative, parameter-free technique for eliminating program bloat. We test it on a classification problem and find that while vastly reducing program size, it does improve generalisation performance. %@ 0-7803-8515-2

@inproceedings{berstein:2004:mpefsgp, abstract = {Program Bloat - the phenomenon of ever-increasing program size during a GP run - is a recognised and widespread problem. Traditional techniques to combat program bloat are program size limitations or parsimony pressure (penalty functions). These techniques suffer from a number of problems, in particular their reliance on parameters whose optimal values it is difficult to a priori determine. In this paper we introduce POPE-GP, a system that makes use of the NSGA-II multiobjective evolutionary algorithm as an alternative, parameter-free technique for eliminating program bloat. We test it on a classification problem and find that while vastly reducing program size, it does improve generalisation performance.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Portland, Oregon}, author = {Bernstein, Yaniv and Li, Xiaodong and Ciesielski, Vic and Song, Andy}, biburl = {https://www.bibsonomy.org/bibtex/2996c27ffbc463a68f1af5b812f31615b/brazovayeye}, booktitle = {Proceedings of the 2004 IEEE Congress on Evolutionary Computation}, interhash = {b95c01cc3654addd7c2eb980896a2081}, intrahash = {996c27ffbc463a68f1af5b812f31615b}, isbn = {0-7803-8515-2}, keywords = {Combinatorial Multiobjective \& algorithms, evolutionary genetic numerical optimization programming,}, month = {20-23 June}, notes = {CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.}, pages = {83--89}, publisher = {IEEE Press}, size = {7 pages}, timestamp = {2008-06-19T17:36:30.000+0200}, title = {Multiobjective Parsimony Enforcement for Superior Generalisation Performance}, url = {http://goanna.cs.rmit.edu.au/~ybernste/papers/Bernstein_CEC_2004.pdf}, year = 2004 }