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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 e-book constitutes the completely refereed post-conference complaints 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 function descriptors for photograph processing and popularity; combinatorial and numerical optimization; laptop studying conception and techniques; clever keep an eye on and automation; wisdom representation/reasoning and specialist platforms; clever computing in development reputation; clever computing in snapshot processing; clever computing in desktop imaginative and prescient; biometrics with purposes to person security/forensic sciences; modeling, idea, and purposes of optimistic platforms; sparse manifold studying equipment and purposes; 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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Besides, Ol cannot violate the precedence relations of the same job. Thus, the assignable idle time interval for can be defined by Ol ' max{C E ( PM ik, j ) ' , C E ( PJ l ) ' } + t l ,k <= min{S iL, j , S L ( SJ l ) ' } . The above moving process is repeated until all critical operations are moved. Let N l be the number of positions to move Ol feasibly, then the total number of moving neighbors of a solution is N total = ∑ Nc l =1 Nl . Moving critical operations will obtain new solutions. For the FJSP, there may be more than one critical path for a schedule.

K ) and xl . n}, l ≠ m ≠ i two particles randomly selected from X m ( k ) and X l (k ) respectively, and xl . ( k ) 's fitness value is better than xm. ( k ) 's. s > 0 is the strength of Baldwinian learning, Pl ∈ (0,1] is the probability of Baldwinian learning, and rand denotes a random number chosen from a uniform distribution on the interval [0,1]. In the Fig. 1, the dashed circles denote contour lines of fitness, the Baldwinian learning generates new particle yij(k), and yij(k)is much closer to the optimal region than xij ( k ) , after several iterations, the particle will converge to the direction of the optimal value.

The PBIL, univariate marginal distribution algorithm (UMDA) and compact GA (CGA) are univariate models, while mutual information maximization for input clustering (MIMIC), combining optimizers with mutual information trees (COMIT) and bivariate marginal distribution algorithm (BMDA) are bivariate models. The factorized distribution algorithm (FDA), extended compact GA (ECGA) and Bayesian optimization algorithm (BOA) are multivariate models. Please refer [7] for more details about the EDA. So far the EDA has been applied to a variety of academic and engineering optimization problems, such as feature selection, cancer classification, quadratic assignment problem, machinery structure design, nurse rostering, and etc [8].

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