基于邻域概率模型的变异算子设计
Designing Competent Mutation Operators via Probabilistic Model Building of Neighborhoods
摘要：
本文提出了一种适应于连锁的选择变异遗传算法（GA），它能快速、可靠、准确地解决难题。概率模型构建过程用于自动识别搜索问题的关键构建块（BB）。变异算子使用连锁群的概率模型在竞争的构建块中找到最佳。主管selectomutative GA成功地解决了有界困难的可加性分离的问题，只需要次二次函数的评价。结果表明，对于可加性分离问题，构建BB方式突变的概率模型的规模为O（2^km^{1.5}），并且需要比选择重组对应物少O（k^{0.5}logm）的函数评估，证实了其他地方报道的理论结果（Sastry & Goldberg，2004）。
英文摘要：
This paper presents a competent selectomutative genetic algorithm (GA), that adapts linkage and solves hard problems quickly, reliably, and accurately. A probabilistic model building process is used to automatically identify key building blocks (BBs) of the search problem. The mutation operator uses the probabilistic model of linkage groups to find the best among competing building blocks. The competent selectomutative GA successfully solves additively separable problems of bounded difficulty, requiring only subquadratic number of function evaluations. The results show that for additively separable problems the probabilistic model building BB-wise mutation scales as O(2^km^{1.5}), and requires O(k^{0.5}logm) less function evaluations than its selectorecombinative counterpart, confirming theoretical results reported elsewhere (Sastry & Goldberg, 2004).
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