| Adaptive Immune Evolutionary Algorithms Based on Immune Network Regulatory Mechanism |
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| 作者姓名： | 何宏 钱峰 |
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| 作者单位： | [1]State-Key Laboratory of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China [2]College of Mechanical and Electronic Engineering, Shanghai Normal University, Shanghai 201418, China |
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| 基金项目： | 国家重点基础研究发展计划(973计划);国家高技术研究发展计划(863计划) |
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| 摘 要： | Based on immune network regulatory mechanism, a new adaptive immune evolutionary algorithm （AIEA） is proposed to improve the performance of genetic algorithms （GA） in this paper. AIEA adopts novel selection operation according to the stimulation level of each antibody. A memory base for good antibodies is devised simultaneously to raise the convergent rapidity of the algorithm and adaptive adjusting strategy of antibody population is used for preventing the loss of the population adversity. The experiments show AIEA has better convergence performance than standard genetic algorithm and is capable of maintaining the adversity of the population and solving function optimization problems in an efficient and reliable way.
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| 关 键 词： | 自适应免疫进化算法 免疫网络 调节机制 刺激水平 |
| 文章编号： | 1672-5220（2007）01-0141-05 |
| 修稿时间： | 2006-10-26 |
Adaptive Immune Evolutionary Algorithms Based on Immune Network Regulatory Mechanism |
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| HE Hong,QIAN Feng.Adaptive Immune Evolutionary Algorithms Based on Immune Network Regulatory Mechanism[J].Journal of Donghua University,2007,24(1):141-145. |
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| Authors: | HE Hong QIAN Feng |
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| Institution: | 1. State-Key Laboratory of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China;College of Mechanical and Electronic Engineering, Shanghai Normal University, Shanghai 201418, China
2. State-Key Laboratory of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China |
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| Abstract: | Based on immune network regulatory mechanism, a new adaptive immune evolutionary algorithm (AIEA) is proposed to improve the performance of genetic algorithms (GA) in this paper. AIEA adopts novel selection operation according to the stimulation level of each antibody. A memory base for good antibodies is devised simultaneously to raise the convergent rapidity of the algorithm and adaptive adjusting strategy of antibody population is used for preventing the loss of the population adversity. The experiments show AIFA has better convergence performance than standard genetic algorithm and is capable of maintaining the adversity of the population and solving function optimization problems in an efficient and reliable way. |
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| Keywords: | evolutionary algorithm immune network adaptation stimulation level |
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