Performance and efficiency of memetic Pittsburgh learning classifier systems.
Performance and efficiency of memetic Pittsburgh learning classifier systems.
复制标题
模因匹兹堡学习分类器系统的性能和效率。
DOI：
10.1162/evco.2009.17.3.307
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发表时间：
2009
影响因子：
6.8
通讯作者：
Bacardit J
中科院分区：
文献类型：
--
作者：
Bacardit J
在本文中，我们根据经验评估了几种本地搜索（LS）机制，这些机制可以启发式编辑分类规则和规则集以提高其性能。研究了两种算子，（1）规则明智算子，它编辑单个规则，（2）规则集明智算子，它从 N 个父母（N≥2）中获取规则来生成新的后代，选择获得最大训练精度的候选规则的最小子集。此外，还研究了将这些算子集成到学习分类器系统的进化周期中的各种方法。 LS 算子和策略的组合集成在匹兹堡方法框架中，我们将其称为模因匹兹堡学习分类器系统的 MPLCS。 MPLCS 使用各种指标进行系统评估。使用多个数据集的目的是确定哪种算子和策略组合可扩展性好、对噪声具有鲁棒性、生成紧凑的解决方案以及使用最少的计算资源来解决问题。
In this paper we empirically evaluate several local search (LS) mechanisms that heuristically edit classification rules and rule sets to improve their performance. Two kinds of operators are studied, (1)rule-wiseoperators, which edit individual rules, and (2) arule set-wiseoperator, which takes the rules fromNparents (N≥ 2) to generate a new offspring, selecting the minimum subset of candidate rules that obtains maximum training accuracy. Moreover, various ways of integrating these operators within the evolutionary cycle of learning classifier systems are studied. The combinations of LS operators and policies are integrated in a Pittsburgh approach framework that we call MPLCS for memetic Pittsburgh learning classifier system. MPLCS is systematically evaluated using various metrics. Several datasets were employed with the objective of identifying which combination of operators and policies scale well, are robust to noise, generate compact solutions, and use the least amount of computational resources to solve the problems.