遺傳算法中突變算子的數(shù)學(xué)分析及改進(jìn)策略
MATHEMATICAL ANALYSIS OF MUTATION OPERATOR AND ITS IMPROVED STRATEGY IN GENETIC ALGORITHMS
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摘要: 本文在簡要介紹遺傳算法的基礎(chǔ)上,通過引入#em/em#位改進(jìn)子空間的概念,對不同情形下突變概率的最優(yōu)選取進(jìn)行了分析,然后采用模糊推理技術(shù)來確定選取突變概率的一般性原則。良好的仿真結(jié)果顯示了本文所提改進(jìn)策略的有效性。
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關(guān)鍵詞:
- 遺傳算法(GA); i位改進(jìn)子空間; 模糊推理
Abstract: This paper analyzes the optimization problem of mutation probability (Pm) in genetic algorithms by applying the definition of i-bit improved sub-space. Then fuzzy reasoning technique is adopted to determine the optimal mutation probability in different conditions. The superior convergence property of the new method is evaluated by applying it to two simulation examples. -
Rudolph G. IEEE Trans. on NN, 1994, NN-5(1): 96-101.[2]Holland J H. Adaptation in Natural and Artificial Systems, ANN Arbor: The Univ. of Michigan Press, 1975.[3]陳根社,陳新梅.信息與控制,1994,23(4): 215-222.[4]Janson D J, Frenzel J F. IEEE Expert, 1993, 8(5): 26-33.[5]Yasumasa I, Hiroaki K. IEICE Tans. Fundamentals, 1994, E77-A(4): 731-735.[6]Tomoharu N.Takeshi A[J].IEICE Trans. Information System.1993, E76-D(6):689-697[7]Zhang Liangjie, Mao Zhihong, Li Yanda. An Improved Genetic Algorithm Based on Combinative Theory Fuzzy Reasoning and its Applications, International Conference on Neural Information Processing, Korea: 1994, 180-185.[8]Hung S L, Adeli H. IEEE Trans on NN, 1994, NN-5(6): 900-909.[9]Grefenstette J J. IEEE Tans. on SMC, 1986, SMC-16(1): 122-128.[10]Goldberg D E. Genetic Algorithms in Search, Optimization and Learning. Addison Wesley Publishing Company, 1989, Chapter 4 and Chapter 5. -
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