Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming
Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming
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DOI：
10.1007/s40747-021-00507-6
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发表时间：
2021-08
影响因子：
5.8
通讯作者：
Libin Hong;John R. Woodward;E. Özcan;Fuchang Liu
中科院分区：
文献类型：
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作者：
Libin Hong;John R. Woodward;E. Özcan;Fuchang Liu
遗传编程（GP）自动设计程序。进化规划（EP）是一种实值全局优化方法。EP使用概率分布作为变异算子，例如高斯分布、柯西分布或Lévy分布。本研究提出一种超启发式的方法，采用GP自动设计不同的变异算子EP。在每代算法中，EP算法可以根据历史信息自适应地探索搜索空间。实验结果表明，与其他EP版本（手动和自动设计）相比，EP与自适应变异算子，所提出的超进化设计，表现出更好的性能。进化计算领域的许多研究人员提倡自适应搜索算子（随着时间的推移而适应），而不是非自适应算子（不随着时间的推移而改变）。本研究的核心动机是，我们可以自动设计自适应变异算子，优于自动设计的非自适应变异算子。
Genetic programming (GP) automatically designs programs. Evolutionary programming (EP) is a real-valued global optimisation method. EP uses a probability distribution as a mutation operator, such as Gaussian, Cauchy, or Lévy distribution. This study proposes a hyper-heuristic approach that employs GP to automatically design different mutation operators for EP. At each generation, the EP algorithm can adaptively explore the search space according to historical information. The experimental results demonstrate that the EP with adaptive mutation operators, designed by the proposed hyper-heuristics, exhibits improved performance over other EP versions (both manually and automatically designed). Many researchers in evolutionary computation advocate adaptive search operators (which do adapt over time) over non-adaptive operators (which do not alter over time). The core motive of this study is that we can automatically design adaptive mutation operators that outperform automatically designed non-adaptive mutation operators.