Self-adaptive mutation in XCSF
Self-adaptive mutation in XCSF
复制标题
XCSF 中的自适应突变
DOI：
10.1145/1389095.1389361
复制
发表时间：
2008
影响因子：
2.1
通讯作者：
P. Lanzi
中科院分区：
文献类型：
--
作者：
Martin Volker Butz;Patrick O. Stalph;P. Lanzi
XCS技术的最新进展表明，自适应突变可以极大地加快XCS的进化进程。此外，最近的出版物表明，XCS也可以成功地应用于挑战实值领域，包括数据挖掘、函数逼近和聚类。在本文中，我们将这两个进展结合起来，研究了具有超椭球条件结构的函数逼近的XCS系统中的自适应突变，本文称为XCSF。研究表明，XCSF解决函数逼近问题的精度、噪声稳健性和泛化能力可与其他统计机器学习技术相媲美，并且XCSF的性能优于添加了线性逼近的简单聚类技术。本文的研究表明，选择合适的自适应变异类型可以进一步提高XCSF的求解性能，使其具有参数无关性和可靠性。我们分析了各种类型的自适应变异，结果表明，XCSF具有自适应变异范围，区分不同的分类器条件值，产生最稳健的性能结果。未来的工作可能会进一步研究自适应值的性质，并可能整合先进的自适应技术。
Recent advances in XCS technology have shown that self-adaptive 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 self-adaptive mutation can further improve XCSF's performance solving problems more parameter independent and more reliably. We analyze various types of self-adaptive mutation and show that XCSF with self-adaptive mutation ranges,differentiated for the separate classifier condition values, yields most robust performance results. Future work may further investigate the properties of the self-adaptive values and may integrate advanced self-adaptation techniques.