Advanced Research on Computer Science and Information by Xiang Xu, Kun Zou (auth.), Gang Shen, Xiong Huang (eds.)

By Xiang Xu, Kun Zou (auth.), Gang Shen, Xiong Huang (eds.)

This two-volume set (CCIS 152 and CCIS 153) constitutes the refereed lawsuits of the overseas convention on laptop technology and knowledge Engineering, CSIE 2011, held in Zhengzhou, China, in may perhaps 2011. The 159 revised complete papers offered in either volumes have been conscientiously reviewed and chosen from quite a few submissions. The papers current unique learn effects which are greatly proper to the idea and purposes of computing device technology and data Engineering and handle a large choice of issues equivalent to algorithms, automation, synthetic intelligence, bioinformatics, laptop networks, machine defense, machine imaginative and prescient, modeling and simulation, databases, information mining, e-learning, e-commerce, e-business, photograph processing, wisdom administration, multimedia, cellular computing, typical computing, open and cutting edge schooling, development attractiveness, parallel computing, robotics, instant networks, and net applications.

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Extra resources for Advanced Research on Computer Science and Information Engineering: International Conference, CSIE 2011, Zhengzhou, China, May 21-22, 2011, Proceedings, Part II

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Cn Abstract. The research status of multi-modal genetic algorithm is summarized. By analysis the mechanisms of Niche Genetic Algorithm (NGA) and Simple Sub-population Genetic Algorithm (SSGA), the faults of them are pointed out and a new Migrating-Based Genetic Algorithm (MBGA) with strategy of excellent sub-population migrating is proposed. The concept of complete convergence of multi-modal genetic algorithm is proposed. Using mathematical methods of Markov chains theory, it is proven that NGA is not complete convergence but MBGA is.

Due to the path-cost function g(n) has taken terrain cost into account, just before assumes all terrain costs are same, so set each node cost is equal. But in the actual game, when calculates g(n), may for the different terrain node designation different cost, thus realizes the lowest path cost, rather than the shortest path. 6 X. Xu and K. Zou Fig. 5. Left Graph has not considered the terrain cost. Right Graph has considered the terrain cost, and set the lawn terrain influence factor equals 1, the mountain road terrain influence factor equals 2.

Spears in 1994, which main idea is that increasing a label for every chromosome, which indicates under the sub-population. The SSGA is easier than NGA, which doesn’t need to assume that the peaks are equidistant and know the distance between peaks. The fitness share of individual is the primary fitness dividing the scale of sub-population, therefore avoiding to compute the distance between individuals, and the operation time is reduced, but the amount of sub-population must more than the number of peaks (it still need to be predicted).

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