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

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) used to be shaped to supply an annual discussion board devoted to the rising and hard subject matters in man made intel- gence, laptop studying, development attractiveness, photograph processing, bioinformatics, and computational biology. It goals to collect researchers and practitioners from either academia and to percentage principles, difficulties, and strategies relating to 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 equipped 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 12 months, the convention centred in most cases at the theories and methodologies in addition to the rising purposes of clever computing. Its objective used to be to unify the image of up to date clever computing innovations as an critical idea that highlights the traits in complex computational intelligence and bridges theoretical examine with purposes. for this reason, the topic for this convention was once “Advanced clever Computing expertise and Applications.” Papers targeting this subject have been solicited, addressing theories, methodologies, and functions in technology and technology.

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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. 001 ). If at i -dimension of the two selected individuals the decision variables content with the former inequation, then we call two individuals i -dimensional δ − similar .

247–259. Springer, Heidelberg (2005) 2. : The Vision of Autonomic Computing. IEEE Computer, 41–50 (January 2003) 3. : An Architectural Approach to Autonomic Computing. In: Proc. of International Conference on Autonomic Computing (ICAC). IEEE Computer Society, Los Alamitos (2004) 4. : An Efficient and Self-Configurable Publish-Subscribe System. , Ni, J. ) GCC 2003. LNCS, vol. 3032, pp. 159–163. Springer, Heidelberg (2004) 5. : Achieving Self-configuration Capability in Autonomic Systems Using Case-Based Reasoning with a New Similarity Measure.

2. Performance results of a sample run of ANN on AFFA dataset In this case study, 20 training cases are used in Matlab neural network tool that executes back propagation feed forward neural network technique. After sensing multiple inputs, weighted inputs are evaluated in the neuron that range from 0 to 1. Learning the training cases requires the hit and trial method to find the best performance by 20 M. J. Khan measuring the root mean squared error (RMSE) and applying different hidden layers. Result of the experiments conducted using 20 test cases in each experiment have been represented in Figure 2.

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