An analysis of matching in learning classifier systems
An analysis of matching in learning classifier systems
复制标题
学习分类器系统中的匹配分析
DOI：
10.1145/1389095.1389359
复制
发表时间：
2008
期刊：
影响因子：
3.3
通讯作者：
D. Loiacono
中科院分区：
文献类型：
--
作者：
Martin Volker Butz;P. Lanzi;Xavier Llorà;D. Loiacono
我们研究规则匹配学习分类系统的问题，涉及二进制和真实的输入。我们考虑三种规则编码：广泛使用的基于字符的编码、基于特定性的编码和Alecsys中使用的二进制编码。我们比较了这三种算法的性能匹配单独和典型的测试问题。单独匹配的结果表明，人口的普遍性以不同的方式影响基于字符串表示的匹配算法的性能。随着一般性的增加，基于特定性的编码变得越来越慢，随着一般性的增加，基于特定性的编码变得越来越快。对典型测试问题的结果表明，基于特定性的表示可以将匹配所需的时间减半，而且二进制编码在最困难的问题上的速度大约快10倍。此外，我们扩展了基于特定性的编码，真正的输入，并提出了一个算法，可以减半的时间需要匹配真实的输入使用基于区间的表示。
We investigate rule matching in learning classifier systems for problems involving binary and real inputs. We consider three rule encodings: the widely used character-based encoding, a specificity-based encoding, and a binary encoding used in Alecsys. We compare the performance of the three algorithms both on matching alone and on typical test problems. The results on matching alone show that the population generality influences the performance of the matching algorithms based on string representations in different ways. Character-based encoding becomes slower and slower as generality increases, specificity-based encoding becomes faster and faster as generality increases. The results on typical test problems show that the specificity-based representation can halve the time required for matching but also that binary encoding is about ten times faster on the most difficult problems. Moreover, we extend specificity-based encoding to real-inputs and propose an algorithm that can halve the time require for matching real inputs using an interval-based representation.