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A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms

机译:遗传算法中模块化的语法遗传规划方法

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The ability of Genetic Programming to scale to problems of increasing difficulty operates on the premise that it is possible to capture regularities that exist in a problem environment by decomposition of the problem into a hierarchy of modules. As computer scientists and more generally as humans we tend to adopt a similar divide-and-conquer strategy in our problem solving. In this paper we consider the adoption of such a strategy for Genetic Algorithms. By adopting a modular representation in a Genetic Algorithm we can make efficiency gains that enable superior scaling characteristics to problems of increasing size. We present a comparison of two modular Genetic Algorithms, one of which is a Grammatical Genetic Programming algorithm, the meta-Grammar Genetic Algorithm (mGGA), which generates binary string sentences instead of traditional GP trees. A number of problems instances are tackled which extend the Checkerboard problem by introducing different kinds of regularity and noise. The results demonstrate some limitations of the modular GA (MGA) representation and how the mGGA can overcome these. The mGGA shows improved scaling when compared the MGA.
机译:遗传编程能够解决日益增加的难题的能力的前提是,可以通过将问题分解为模块层次结构来捕获存在于问题环境中的规则。作为计算机科学家,更一般地说,作为人类,我们倾向于在解决问题时采用类似的分而治之的策略。在本文中,我们考虑对遗传算法采用这种策略。通过在遗传算法中采用模块化表示,我们可以提高效率,从而能够针对大小增加的问题提供出色的缩放特性。我们比较了两种模块化遗传算法,其中一种是语法遗传编程算法,即元语法遗传算法(mGGA),该算法生成二进制字符串语句而不是传统的GP树。解决了许多问题实例,这些实例通过引入不同种类的规律性和噪声扩展了Checkerboard问题。结果证明了模块化GA(MGA)表示的某些局限性以及mGGA如何克服这些局限性。与MGA相比,mGGA显示出改进的缩放比例。

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