Multiobjective Parsimony Enforcement for Superior Generalisation Performance

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{berstein:2004:mpefsgp,

• title = "Multiobjective Parsimony Enforcement for Superior Generalisation Performance",
• author = "Yaniv Bernstein and Xiaodong Li and Vic Ciesielski and Andy Song",
• pages = "83--89",
• booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation",
• year = "2004",
• publisher = "IEEE Press",
• month = "20-23 " # jun,
• address = "Portland, Oregon",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Multiobjective evolutionary algorithms, Combinatorial \& numerical optimization",
• URL = "http://goanna.cs.rmit.edu.au/~ybernste/papers/Bernstein_CEC_2004.pdf",
• DOI = "doi:10.1109/CEC.2004.1330841",
• size = "7 pages",
• 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.",
• notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.",

}

Genetic Programming entries for Yaniv Bernstein Xiaodong Li Victor Ciesielski Andy Song

Citations