By Yongquan Zhou, Yanlian Du, Zhengxin Huang (auth.), De-Shuang Huang, Martin McGinnity, Laurent Heutte, Xiao-Ping Zhang (eds.)
The overseas convention on clever Computing (ICIC) was once shaped to supply an annual discussion board devoted to the rising and not easy issues in synthetic intel- gence, computer studying, trend popularity, photograph processing, bioinformatics, and computational biology. It goals to compile researchers and practitioners from either academia and to percentage principles, difficulties, and suggestions regarding the m- tifaceted facets of clever computing. ICIC 2010, held in Changsha, China, August 18-21, 2010, constituted the sixth - ternational convention on clever Computing. It outfitted upon the good fortune 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 yr, the convention targeted usually at the theories and methodologies in addition to the rising functions of clever computing. Its goal was once to unify the image of up to date clever computing recommendations as an quintessential idea that highlights the developments in complex computational intelligence and bridges theoretical examine with purposes. for that reason, the subject matter for this convention used to be “Advanced clever Computing know-how and Applications.” Papers targeting this topic have been solicited, addressing theories, methodologies, and purposes in technological know-how and technology.
Read Online or Download Advanced Intelligent Computing Theories and Applications: 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings PDF
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Additional resources for Advanced Intelligent Computing Theories and Applications: 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings
The algorithm trains a classifier h t in response. The distribution wt i is updated after each cycle according to the prediction results on the training samples. Easy samples that are correctly classified get lower weights, and hard samples that are misclassified get higher weights. Thus, AdaBoost focuses on the samples with higher weights, which seem to be harder for the algorithm. This process continues for T cycles, and finally, AdaBoost linearly combines the component classifiers into a single final hypothesis H ( x) .
However, the precision of the latter is confined by the dimension of the search space and the size of the orthogonal table, the former is not. 3 GPS Operator If we select two individuals X1 =( x11, x12 ,…, x1n ) X 2 =( x12 , x22 ,…, xn2 ) , T :⎡ x X , X , x X , X ⎤ , with ⎣ 1 2 1 2⎦ ， from then parents define at a local randomly, search let space ⎧ 1 2 1 2 1 2 ⎪ x X1, X 2 =(min( x1 , x1 ),min( x2 , x2 ),…,min( xn , xn )) . ⎨ ⎪ x X , X =(max( x1, x 2 ),max( x1 , x 2 ),…,max( x1n , xn2 )) 1 1 2 2 ⎩ 1 2 (8) In order to measure the degree of the difference between two decision variables at same dimension respectively.
The proposed method finds better “medium” results in five problems (G2,G3,G4,G5 and G7) and similar “medium” results in other three (G1, G6 and G8). It also finds better “worst” results in eight problems, except for problem G6 in which the method in this paper has similar “worst” result with GFCO. Table 1. 7500 38 Y. Liu and S. Li Table 2. e. the number of FFEs) is concerned, GFCO has the minimum computational cost (50 000 FFEs) for all the test functions, HGPS has the medium computational cost (180 000FFEs) for all the test functions, however, SAFF has the highest computational cost (1 400 000 FFEs) for all the test functions.