Advances in Computer Science and Engineering: 13th by Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghasem Miremadi,

By Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghasem Miremadi, Shaahin Hessabi

This e-book constitutes the revised chosen papers of the thirteenth foreign CSI machine convention, CSICC 2008 hung on Kish Island, Iran, in March 2008. The eighty four common papers offered including sixty eight poster displays have been conscientiously reviewed and chosen from a complete of 426 submissions.

The papers are prepared in topical sections on learning/soft computing, set of rules thought, SoC and NoC, wireless/sensor networks, video processing and comparable themes, processor structure, AI/robotics/control, clinical photo processing, p2p/cluster/grid structures, cellular advert hoc networks, net, sign processing/speech processing, misc, safety, photograph processing purposes in addition to VLSI.

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5. Successful operaation of the posed restart algorithm to evade local optimums heuristic is the same as the first one without β modulation, and finally, No GA resstart represents the algorithm witthout any restart process. Table 1 shows a good performance p of our algorithm specially compared with the same procedure without restart algorithm. Furthermore, the average number of innciG restart algorithm granted new initial survivor sooludents that the introduced Genetic tion to ACO algorithm is stated in Table 2.

Springer-Verlag Berlin Heidelberg 2008 18 M. Neshati, H. Abolhassani, and H. Fatemi Fig. 1. Compound model for Extracting IS_A Relations A classic way for finding ‘is-a’ relations between words is by searching lexicosyntactic patterns in large text corpus. Although this approach results in high values of precision but it doesn’t yield promising values of recall. In this paper we use the web, the most comprehensive text corpus, in order to improve the low values of recall. Therefore, we use the search engines for searching web pages.

N. Araabi AS Local Best Tour (AS-LBT) [6], is another improved kind of AS, in which only local information is used to reinforce trails. It means that each ant updates its trail by the best tour it has found to date. This approach shows more diversity than AS. Some other improvements in the field of ACO are the Multiple Ant Colonies Algorithms [9], which exploits interactions between colonies, Population-based ACO (PACO), which makes up a special population of good solutions, and Omicron ACO (OA), which is inspired by MMAS and elitist strategy.

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