Download Advanced Intelligent Computing Theories and Applications: by Yongquan Zhou, Yanlian Du, Zhengxin Huang (auth.), De-Shuang PDF

By Yongquan Zhou, Yanlian Du, Zhengxin Huang (auth.), De-Shuang Huang, Martin McGinnity, Laurent Heutte, Xiao-Ping Zhang (eds.)

The foreign convention on clever Computing (ICIC) used to be shaped to supply an annual discussion board devoted to the rising and not easy issues in synthetic intel- gence, laptop studying, trend reputation, photo processing, bioinformatics, and computational biology. It goals to collect researchers and practitioners from either academia and to proportion rules, difficulties, and ideas relating to the m- tifaceted points of clever computing. ICIC 2010, held in Changsha, China, August 18-21, 2010, constituted the sixth - ternational convention on clever Computing. It equipped upon the luck of ICIC 2009, ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005, that have been held in Ulsan, Korea, Shanghai, Qingdao, Kunming and Hefei, China, respectively. This 12 months, the convention focused generally at the theories and methodologies in addition to the rising functions of clever computing. Its objective used to be to unify the image of latest clever computing ideas as an imperative idea that highlights the traits in complicated computational intelligence and bridges theoretical learn with purposes. accordingly, the subject for this convention used to be “Advanced clever Computing know-how and Applications.” Papers concentrating on this topic have been solicited, addressing theories, methodologies, and functions in technology and technology.

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Additional info for Advanced Intelligent Computing Theories and Applications: 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings

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Assume individual X1 and individual X 2 are not δ − similar among the first t dimensional decision variables, but the last n−t -dimensional decision variables are δ − similar . In the t -dimensional local search space we can find the Good Points as follows. ,n} ,with ri =2cos 2πp i , 1≤i ≤t ,where p is the minimum prime number which content with p ≥2t +3 and {rt *k } is i 1≤i ≤t ). the decimal fraction of ri *k (or with ri =e , Good Points Set principle is based on unit hypercube or hypersphere, in order to t t mapping n Good Points from unit space H :[0,1] to the search space T :⎡⎣ xL,M , xL,M ⎤⎦ of the questions, we define the function f :H →T by f ( ri *k )= min( x1i , xi2 ) +{ri *k }*(max( x1i , xi2 )− min( x1i , xi2 )) , (9) with 1≤i ≤t , 1≤ k ≤ n , where {ri *k } is the same as the former defined.

And its precision isn’t confined by the space dimension. Fig. 2. Pseudocode of the temporary crossover operator before GPS crossover operator used by our approach. 5 . But when come to such problems that scope of decision variables are very large and the number of the points generated by the GPS crossover operator is very small, that is, the points is distributed sparsely in the search space. Evidently, the best effect can be reached when the points in unit search space keep a certain probability.

The decimal fraction of ri *k (or with ri =e , Good Points Set principle is based on unit hypercube or hypersphere, in order to t t mapping n Good Points from unit space H :[0,1] to the search space T :⎡⎣ xL,M , xL,M ⎤⎦ of the questions, we define the function f :H →T by f ( ri *k )= min( x1i , xi2 ) +{ri *k }*(max( x1i , xi2 )− min( x1i , xi2 )) , (9) with 1≤i ≤t , 1≤ k ≤ n , where {ri *k } is the same as the former defined. Now, we employ an example to illustrate the above process. Suppose that two individuals are selected from parent population with the following vector values of X1 , X 2 and four offspring are to be generated through the GPS crossover operator.

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