Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis
Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis
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DOI：
10.1007/s00500-016-2126-x
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发表时间：
2016-04
期刊：
影响因子：
4.1
通讯作者：
Pietro A. Consoli;Yi Mei;Leandro L. Minku;X. Yao
中科院分区：
文献类型：
--
作者：
Pietro A. Consoli;Yi Mei;Leandro L. Minku;X. Yao
用于在进化算法中识别最合适的变异算子的自适应机制几乎完全依赖于后代的适应度的测量，这可能不足以评估算子的最优性（例如，在具有高度中立性的景观中）。本文提出了一种新的自适应算子选择机制，它使用了一套四个适应景观分析技术和在线学习算法，动态加权多数，提供更详细的信息，搜索空间，以更好地确定最合适的交叉算子。对有能力约束的弧路径问题的实验分析表明，不同的交叉算子在搜索过程中表现不同，自适应地选择合适的交叉算子可以得到更好的结果。
Self-adaptive mechanisms for the identification of the most suitable variation operator in evolutionary algorithms rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel adaptive operator selection mechanism which uses a set of four fitness landscape analysis techniques and an online learning algorithm, dynamic weighted majority, to provide more detailed information about the search space to better determine the most suitable crossover operator. Experimental analysis on the capacitated arc routing problem has demonstrated that different crossover operators behave differently during the search process, and selecting the proper one adaptively can lead to more promising results.