Improving Generalisation Performance Through Multiobjective Parsimony Enforcement

This paper describes 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, the technique does improve generalisation performance. 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 work we study the performance of POPE-GP, a new algorithm that uses the NSGA-II multiobjective algorithm as the basis for parsimony enforcement. We are especially interested in finding out if small solutions generalise better than large solutions. To achieve this, we co...

• Type: Conference
• Year: 2004
• Where: GECCO
• Authors: Yaniv Bernstein, Xiaodong Li, Victor Ciesielski, Andy Song
• Keywords: GECCO 2004, Generalisation Performance, Program Bloat, Program Size