文章基本信息

标题：Hybridization of Genetic Algorithm and Neural Network for Optimization Problem
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作者：Gaurang Panchal ; Devyani Panchal
期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2015
卷号：4
期号：4
页码：1507-1511
出版社：Shri Pannalal Research Institute of Technolgy
摘要：The use of both, genetic algorithms and artificial neural networks, were originally motivated by the astonishing success of these concepts in their biological counterparts. Despite their totally deferent approaches, both can merely be seen as optimization methods, which are used in a wide range of applications. "Genetic algorithms (GA) are good at taking large, potentially huge search spaces and navigating them, looking for optimal combinations of things, solutions you would find difficult to accomplish." A genetic algorithm (GA) is an iterative search, optimization and adaptive machine learning technique premised on the principles of Natural selection. They are capable to finding solution to NP hard Problems. Neural Networks utilizing back propagation based learning have promisingly showed results to a vast variety of function and problems. TSP is one such classical problem for computation.
关键词：Genetic Algorithm; Neural Network; Weight ; Optimization; Neural Network Parameter