Download Advanced Intelligent Computing Theories and Applications by De-Shuang Huang, Donald C. Wunsch, Daniel S. Levine, PDF

By De-Shuang Huang, Donald C. Wunsch, Daniel S. Levine, Kang-Hyun Jo

This ebook - at the side of the 2 volumes LNCS 5226 and LNAI 5227 - constitutes the refereed court cases of the Fourth overseas convention on clever Computing, ICIC 2008, held in Shanghai, China in September 2008. The clever computing expertise encompasses a variety of thoughts corresponding to man made intelligence, perceptual and development acceptance, evolutionary and adaptive computing, informatics theories and purposes, computational neuroscience and bioscience, tender computing, case established and limited reasoning, brokers, networking and laptop supported co-operative operating, human laptop interface matters. ICIC topic unifies the image of up to date clever computing options as an quintessential idea that highlights the traits in complicated computational intelligence and bridges theoretical learn with purposes.

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Extra resources for Advanced Intelligent Computing Theories and Applications With Aspects of Contemporary Intelligent Computing Techniques: 4th International Conference on ... in Computer and Information Science)

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Therefore, milk products should be shelved next to bread products. Such information is called “Association Rules”. Here is how it is described: milk→bread[minsup=2% minconf = 80%]. There are two important parameters in Association Rules, which are support and confidence. These two parameters are used to evaluate if Association Rules can meet the expectations of the users. The most common algorithms to obtain Association Rules include Apriori algorithm, DHP, AprioriTid, AprioriHybrid, Boolean, FP-Tree, ICI and AIM.

The association of each solution to a fitness value is done through the fitness function. We associate the link cost and link weight to each individual through the function Φ. The evaluation function is complex and computationally demanding, as it includes the process of the shortest path and best routing, needed to determine the arc loads resulting from a given set of weights. This evaluation function is the computational bottleneck of the algorithm. Another basic computation needed by the genetic algorithm is the comparison of different solutions.

1 provides a more detailed description of the whole algorithm in general and of the proposed operator in particular. 4 X. Yan, Q. Wu, and Z. Cai Random initialization of the population P While (not satisfied termination condition) do begin for each individual S i ∈ P do begin S ' ← Si Select (randomly) a Node C from S ' repeat {begin if (rand () ≤ p) select the node C ' from the remaining nodes in S ' else {Select (randomly) an individual in P assign to C ' the next node to the node C in the select individual } if (the next node or the previous node of node C in S ' is C ' ) exit repeat loop invert the gene node from the next node of C to the node C ' in S ' C ← C' } end if (eval ( S ' ) ≤ eval ( S i )) Si ← S ' end end Fig.

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