Abstract We report a series of experiments that use semantic-based local search within a multiobjective genetic programming (GP) framework. We compare various ways of selecting target subtrees for local search as well as different methods for performing that search; we have also made comparison with the random desired operator of Pawlak et al. using statistical hypothesis testing. We find that a standard steady state or generational GP followed by a carefully-designed single-objective GP implementing semantic-based local search produces models that are mode accurate and with statistically smaller (or equal) tree size than those generated by the corresponding baseline GP algorithms. The depth fair selection strategy of Ito et al. is found to perform best compared with other subtree selection methods in the model refinement.

中文翻译：

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基于语义的多目标遗传规划局部搜索方法比较

摘要 我们报告了一系列在多目标遗传编程 (GP) 框架内使用基于语义的局部搜索的实验。我们比较了为本地搜索选择目标子树的各种方法以及执行该搜索的不同方法；我们还与 Pawlak 等人的随机期望算子进行了比较。使用统计假设检验。我们发现，标准稳态或分代 GP 后跟一个精心设计的单目标 GP 实现基于语义的局部搜索，生成的模型模式准确，并且树大小在统计上小于（或等于）相应的基线 GP 生成的模型算法。Ito 等人的深度公平选择策略。发现在模型细化中与其他子树选择方法相比表现最佳。