基于树的非编码节点语法演化

Marina de la Cruz López, O. Garnica, J. Hidalgo
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引用次数: 0

摘要

语法引导遗传规划是遗传规划的一种,它在搜索空间的探索中使用语法来限制解。存在着不同的语法引导遗传编程的表示,每一种都具有影响进化过程如何发展的特定属性。我们提出了一种新的表示方法,即使用具有非编码节点的树型结构来表示群体中的个体,即具有非编码节点的基于树的语法进化。每个树的节点都有一组子节点和一个关联的编号,这些编号决定哪些用于解码解决方案,哪些是非编码节点。这种表示增加了个体的大小和复杂性,同时对解决方案空间进行了更详尽的探索。我们将我们的建议的性能与11多路复用器基准的最先进的遗传规划算法进行比较,显示出令人鼓舞的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tree-Based Grammatical Evolution with Non-Encoding Nodes
Grammar-guided genetic programming is a type of genetic programming that uses a grammar to restrict the solutions in the exploration of the search space. Different representations of grammar-guided genetic programming exist, each with specific properties that affect how the evolutionary process is developed. We propose a new representation that uses a tree structure with non-encoding nodes for the individuals in the population, a.k.a. Tree-Based Grammatical Evolution with Non-Encoding Nodes. Each tree's node has a set of children nodes and an associated number that determines which are used in decoding the solution and which are non-encoding nodes. This representation increases the size and complexity of the individuals while performing a more exhaustive exploration of the solution space. We compare the performance of our proposal with state-of-the-art genetic programming algorithms for the 11-multiplexer benchmark, showing encouraging results.
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