%0 Conference Paper %1 svangard:2003:ugpwnppoefpo %A Svangard, Nils %A Nordin, Peter %A Lloyd, Stefan %B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003 %C Canberra %D 2003 %E Sarker, Ruhul %E Reynolds, Robert %E Abbass, Hussein %E Tan, Kay Chen %E McKay, Bob %E Essam, Daryl %E Gedeon, Tom %I IEEE Press %K algorithms, genetic programming %P 1014--1017 %T Using genetic programming with negative parsimony pressure on exons for portfolio optimization %X Traditionally Parsimony Pressure has been used with Genetic Programming to reduce the complexity of solutions analogous to the principle of Occam's Razor. But there have been several signs from previous experiments that this reduces the quality of the solutions. In an attempt to counteract this we presents one of the first experiments that try to apply negative parsimony pressure on genetic programming, ie. we prefer complex solutions rather than simpler ones. This system is then applied on a financial portfolio optimisation problem to test it's performance on real world data. Our results indicate that negative parsimony pressure work better than regular parsimony pressure on average, and it's almost always better to use some kind of parsimony pressure than not. %@ 0-7803-7804-0

@inproceedings{svangard:2003:ugpwnppoefpo, abstract = {Traditionally Parsimony Pressure has been used with Genetic Programming to reduce the complexity of solutions analogous to the principle of Occam's Razor. But there have been several signs from previous experiments that this reduces the quality of the solutions. In an attempt to counteract this we presents one of the first experiments that try to apply negative parsimony pressure on genetic programming, ie. we prefer complex solutions rather than simpler ones. This system is then applied on a financial portfolio optimisation problem to test it's performance on real world data. Our results indicate that negative parsimony pressure work better than regular parsimony pressure on average, and it's almost always better to use some kind of parsimony pressure than not.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Canberra}, author = {Svangard, Nils and Nordin, Peter and Lloyd, Stefan}, biburl = {https://www.bibsonomy.org/bibtex/2e4b3aa1e4146b552c2bf5baf19382928/brazovayeye}, booktitle = {Proceedings of the 2003 Congress on Evolutionary Computation CEC2003}, editor = {Sarker, Ruhul and Reynolds, Robert and Abbass, Hussein and Tan, Kay Chen and McKay, Bob and Essam, Daryl and Gedeon, Tom}, interhash = {30583c75df949c11546a1528c073fd29}, intrahash = {e4b3aa1e4146b552c2bf5baf19382928}, isbn = {0-7803-7804-0}, keywords = {algorithms, genetic programming}, month = {8-12 December}, notes = {CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.}, organisation = {IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)}, pages = {1014--1017}, publisher = {IEEE Press}, publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA}, timestamp = {2008-06-19T17:52:29.000+0200}, title = {Using genetic programming with negative parsimony pressure on exons for portfolio optimization}, year = 2003 }