This article describes a new genetic-programming-based optimization method using a multi-gene approach along with a niching strategy and periodic domain constraints. The method is referred to as Niching MG-PMA, where MG refers to multi-gene and PMA to parameter mapping approach. Although it was designed to be a multimodal optimization method, recent tests have revealed its suitability for unimodal optimization. The definition of Niching MG-PMA is provided in a detailed fashion, along with an in-depth explanation of two novelties in our implementation: the feedback of initial parameters and the domain constraints using periodic boundary conditions. These ideas can be potentially useful for other optimization techniques. The method is tested on the basis of the CEC’2015 benchmark functions. Statistical analysis shows that Niching MG-PMA performs similarly to the winners of the competition even without any parametrization towards the benchmark, indicating that the method is robust and applicable to a wide range of problems.

中文翻译：

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使用具有周期性边界条件的基于遗传编程的方法进行单峰优化

本文描述了一种新的基于遗传编程的优化方法，该方法使用多基因方法以及生态位策略和周期性域约束。该方法被称为Niching MG-PMA，其中MG是指多基因，PMA是指参数映射方法。尽管它被设计为一种多峰优化方法，但最近的测试表明它适用于单峰优化。以详细的方式提供了 Niching MG-PMA 的定义，并深入解释了我们实现中的两个新颖之处：初始参数的反馈和使用周期性边界条件的域约束。这些想法可能对其他优化技术有用。该方法是在 CEC'2015 基准函数的基础上进行测试的。