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首页> 外文期刊>International Journal of Innovative Computing Information and Control >K-CUT CROSSOVER USING GRAPH THEORY IN GENETIC NETWORK PROGRAMMING
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K-CUT CROSSOVER USING GRAPH THEORY IN GENETIC NETWORK PROGRAMMING

机译：遗传网络规划中基于图形理论的K-CUT交叉

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摘要

In this study, we focus on Genetic Network Programming (GNP) which is the graph-based evolutionary algorithm. Similar to Genetic Algorithm (GA) and Genetic Programming (GP), GNP applies genetic operators to an individual, which is represented by a directed graph, in order to solve a given problem. GNP is usually applied to automatic generation of programs which control a mobile robot. Since the crossover exchanges a sub-graph of parent individuals, a selection of a sub-graph is an important factor. Some selection methods are proposed in previous work. However, the selection method based on the graph theory is not proposed even though the individual is represented by a graph. In this study, we propose a k-cut crossover based on the graph theory. The proposed k-cut crossover selects a sub-graph by using a minimum k-cut algorithm which finds a minimum graph partition on weighted graph. We applied the GNP with the k-cut crossover to the automatic generation of programs in the tileworld, and the experimental result shows the advantage of the k-cut crossover.
机译：在这项研究中，我们专注于遗传网络编程（GNP），它是基于图的进化算法。与遗传算法（GA）和遗传编程（GP）相似，GNP将遗传算子应用于有向图表示的个体，以解决给定的问题。 GNP通常用于自动生成控制移动机器人的程序。由于分频器交换父母个人的一个子图，因此选择一个子图是重要的因素。在先前的工作中提出了一些选择方法。但是，即使以图表示个人，也没有提出基于图论的选择方法。在这项研究中，我们提出了基于图论的k-cut交叉。提出的k割交叉使用最小k割算法选择子图，该算法在加权图上找到最小图分区。我们将带有k切割交叉的GNP应用于tileworld中程序的自动生成，实验结果显示了k切割交叉的优势。

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