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.

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 Gaussian、Cauchy 或 Lévy 分布。本研究提出了一种超启发式方法，该方法采用 GP 为 EP 自动设计不同的变异算子。在每一代，EP 算法都可以根据历史信息自适应地探索搜索空间。实验结果表明，由所提出的超启发式设计的具有自适应变异算子的 EP 表现出优于其他 EP 版本（手动和自动设计）的性能。许多进化计算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（不随时间改变）。本研究的核心动机是我们可以自动设计自适应变异算子，其性能优于自动设计的非自适应变异算子。