A Self-Adaptive XCS
A Self-Adaptive XCS
复制标题
自适应XCS
DOI：
10.1007/3-540-48104-4_5
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发表时间：
2001
影响因子：
0.9
通讯作者：
L. Bull
中科院分区：
文献类型：
--
作者：
Jacob Hurst;L. Bull
自适应已被广泛用于各种形式的进化计算中的参数控制。这个概念最初是在进化策略中引入的，现在它经常被用来控制遗传算法。本文介绍了一个自适应的变异率和学习率的XCS分类器系统。自适应已经被用于基于强度的学习分类器系统ZCS。这种自适应ZCS在动态Woods环境中表现出明显的性能改进，并对其强化学习参数进行了稳定的适应。在本文中，XCS的实验进行了伍兹2，截断版本的伍兹14环境和动态伍兹环境。XCS在动态Woods 14环境中的性能良好，当环境受到干扰时，性能损失很小。使用自适应突变率并不能帮助或改善这种行为。XCS已经被证明在Woods 14环境和其他长规则链环境中表现不佳。使用自适应突变率显着提高这些长规则链环境中的性能。在Woods 14-12中自适应学习率的尝试也未能实现令人满意的系统性能。
Self-adaptation has been used extensively to control parameters in various forms of evolutionary computation. The concept was first introduced with evolutionary strategies and it is now often used to control genetic algorithms. This paper describes the addition of a self-adaptive mutation rate and learning rate to the XCS classifier system. Self-adaptation has been used before in the strength based learning classifier system ZCS. This self-adaptive ZCS demonstrated clear performance improvements in a dynamic Woods environment and stable adaptation of its reinforcement learning parameters. In this paper experiments with XCS are carried out in Woods 2, a truncated version of the Woods 14 environment and a dynamic Woods environment. Performance of XCS in the dynamic Woods 14 environment is good with little loss of performance when the environment is perturbed. Use of an adaptive mutation rate does not help or improve on this behavior. XCS has already been shown to perform poorly in the Woods 14 environment, and other long rule chain environments. Use of an adaptive mutation rate is shown to increase performance significantly in these long rule chain environments. Attempts to also self-adapt the learning rate in Woods 14-12 fail to achieve satisfactory system performance.