Self-adaptive mutation in XCSF

Recent advances in XCS technology have shown that selfadaptive mutation can be highly useful to speed-up the evolutionary progress in XCS. Moreover, recent publications have shown that XCS can also be successfully applied to challenging real-valued domains including datamining, function approximation, and clustering. In this paper, we combine these two advances and investigate self-adaptive mutation in the XCS system for function approximation with hyperellipsoidal condition structures, referred to as XCSF in this paper. It has been shown that XCSF solves function approximation problems with an accuracy, noise robustness, and generalization capability comparable to other statistical machine learning techniques and that XCSF outperforms simple clustering techniques to which linear approximations are added. This paper shows that the right type of selfadaptive mutation can further improve XCSF’s performance solving problems more parameter independent and more reliably. We analyze vario...

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Martin V. Butz, Patrick O. Stalph, Pier Luca Lanzi
• Keywords: Function Approximation, GECCO 2008, Optimization, Self-adaptive Mutation, Selfadaptive Mutation