Genetic Network Programming with Simplified Genetic Operators
Genetic Network Programming with Simplified Genetic Operators
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DOI：
10.1007/978-3-642-42042-9_7
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
Xianneng Li;Wen He;K. Hirasawa
中科院分区：
文献类型：
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作者：
Xianneng Li;Wen He;K. Hirasawa
最近，一种新的类型的进化算法（EA），称为遗传网络编程（GNP），已被提出。GNP受人脑复杂结构的启发，开发了一种独特的有向图结构，用于其个体表示，从而显示出对一系列复杂问题建模的出色表达能力。本文致力于揭示GNP的独特性。因此，简化的遗传算子被提出来突出GNP的这些特征，减少其计算工作量并提供更好的结果。实验结果证实了它的有效性超过原来的GNP和几个国家的最先进的算法。
Recently, a novel type of evolutionary algorithms (EAs), called Genetic Network Programming (GNP), has been proposed. Inspired by the complex human brain structures, GNP develops a distinguished directed graph structure for its individual representations, consequently showing an excellent expressive ability for modelling a range of complex problems. This paper is dedicated to reveal GNP’s unique features. Accordingly, simplified genetic operators are proposed to highlight such features of GNP, reduce its computational effort and provide better results. Experimental results are presented to confirm its effectiveness over original GNP and several state-of-the-art algorithms.