Download Advanced Intelligent Computing Theories and Applications. by Gang Zhou, Ling Wang, Ye Xu, Shengyao Wang (auth.), PDF

By Gang Zhou, Ling Wang, Ye Xu, Shengyao Wang (auth.), De-Shuang Huang, Yong Gan, Phalguni Gupta, M. Michael Gromiha (eds.)

This publication constitutes the completely refereed post-conference lawsuits of the seventh foreign convention on clever Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. The ninety four revised complete papers awarded have been conscientiously reviewed and chosen from 832 submissions. The papers are prepared in topical sections on clever computing in scheduling; neighborhood characteristic descriptors for snapshot processing and popularity; combinatorial and numerical optimization; desktop studying thought and techniques; clever keep watch over and automation; wisdom representation/reasoning and specialist structures; clever computing in development popularity; clever computing in snapshot processing; clever computing in laptop imaginative and prescient; biometrics with purposes to person security/forensic sciences; modeling, conception, and purposes of confident platforms; sparse manifold studying equipment and functions; advances in clever info processing.

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Extra info for Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence: 7th International Conference, ICIC 2011, Zhengzhou, China, August 11-14, 2011, Revised Selected Papers

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The element pij (t ) of operation probability matrix A1 represents the probability that job J j appears before or in position i of the sequence vector at generation t. The value of pij refers to the importance of a job when scheduling the operations on machines. For all i ( i = 1,2,L, To ) and j ( j = 1,2,L, n ), pij is initialized to pij (0) = 1 / n , which ensures that the whole solution space can be sampled uniformly. The element qijk (t ) of machine probability matrix A2 represents the probability that operation Oi , j is processed on machine M k at generation t.

To generate a new solution, the operation sequence vector should be generated first. For every position i, job J j is selected with the probability pij . If job J j has already appeared n j times in the operation sequence vector, it means the processing procedure of job J j is completed. So, the whole column p1 j , p 2 j ,L, pTo j of probability matrix A1 will be set as zero. An Estimation of Distribution Algorithm for the Flexible Job-Shop Scheduling Problem 13 Similarly, the machine assignment vector is generated according to probability matrix A2 .

Since the makespan is no shorter than any possible critical path, the makespan may be improved only by moving the critical operations. , N c ) be the critical operation to be moved, where N c is the total number of critical operations of a solution. Moving Ol is to delete it from its current position and then to insert it at another feasible position. Obviously, the makespan of the new solution is no larger than the old one. If Ol is assigned before Oi , j on machine M k , it can be started as 14 S.

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