Investigation of self-organizing map for genetic algorithm |
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| Affiliation: | 1. College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350108, China;2. School of Physics and Optoelectronic Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;1. Institute of Information Science, Beijing jiaotong University, Beijing 100044, China;2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;3. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;4. National Key Lab of Integrated Information System Technology, Institute of Software, Chinese Academy of Sciences, Beijing 100080, China;1. Department of Computer Science, Hangzhou Normal University, Hangzhou 311121, China;2. Department of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK;3. Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China;1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;2. Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran;3. Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq |
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| Abstract: | This paper describes self-organizing maps for genetic algorithm (SOM-GA) which is the combinational algorithm of a real-coded genetic algorithm (RCGA) and self-organizing map (SOM). The self-organizing maps are trained with the information of the individuals in the population. Sub-populations are defined by the help of the trained map. The RCGA is performed in the sub-populations. The use of the sub-population search algorithm improves the local search performance of the RCGA. The search performance is compared with the real-coded genetic algorithm (RCGA) in three test functions. The results show that SOM-GA can find better solutions in shorter CPU time than RCGA. Although the computational cost for training SOM is expensive, the results show that the convergence speed of SOM-GA is accelerated according to the development of SOM training. |
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