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 not easy themes in synthetic intel- gence, computer studying, development popularity, snapshot processing, bioinformatics, and computational biology. It goals to assemble researchers and practitioners from either academia and to percentage principles, difficulties, and options concerning 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 outfitted 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 yr, the convention focused typically 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 strategies as an vital idea that highlights the developments in complex computational intelligence and bridges theoretical learn with functions. for that reason, the subject for this convention used to be “Advanced clever Computing know-how and Applications.” Papers targeting this subject have been solicited, addressing theories, methodologies, and functions in technology and technology.
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Extra info for Advanced Intelligent Computing Theories and Applications: 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings
All equality constraints h( j ) (regardless of the value of x ) are considered active at all point of Ω . -S. Huang et al. ): ICIC 2010, CCIS 93, pp. 30–39, 2010. © Springer-Verlag Berlin Heidelberg 2010 Hybrid Good Point Set Evolutionary Strategy for Constrained Optimization 31 Search approach is an important aspect of COEAs. Much previous work has been done on constraint-handling techniques. However, most evolutionary algorithms (EAs) still suffer from the “curse of dimensionality”, which implies that their performance deteriorates rapidly as the dimensionality of the search space increases .
Hot swapping  is another technique that can be used for self-configuration in dynamic online environment. Data clustering and machine learning  techniques are used to group together the related groups for the configuration. That for example in case of system registry will help in accurate configuration settings and allocation of resources for self configuration etc. Table 1 summarizes different techniques that have been utilized to achieve selfconfiguration. 18 M. J. Khan Table 1. Self Configuration using different techniques Techniques Machine Learning Weighted Graphs CBR Hot Swapping Classify Data Yes Yes Yes Yes Retain Option No Yes Yes Yes Evolve Dataset No No No Yes 3 ANN Based Self-configuration Approach Artificial Neural Networks (ANN) , , , ,  are inspired by biological cells relating to the behavior of the brain as a network of units called neurons.
Y. -H. -Y. Gan  For each feature j , Random Forests is used to train a classifier h j ( x i ) . a) b) c) For i = 1, ⋅⋅⋅, K , a vector θi is generated. Use CART algorithm to construct tree h( x,θi ) . Each tree casts 1 vote for the most popular class at x , the class at x is predicted by selecting the class with max votes. Return a hypothesis h j ( x) .  Update the weights wt +1, i = wt , i β t1− e i where e i = 0 if example (4) xi is classified correctly, otherwise, e i = 1 . β t = εt 1− ε .
Advanced Intelligent Computing Theories and Applications: 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings by Yongquan Zhou, Yanlian Du, Zhengxin Huang (auth.), De-Shuang Huang, Martin McGinnity, Laurent Heutte, Xiao-Ping Zhang (eds.)