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 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 equipped in topical sections on clever computing in scheduling; neighborhood characteristic descriptors for picture processing and popularity; combinatorial and numerical optimization; desktop studying thought and strategies; clever keep watch over and automation; wisdom representation/reasoning and professional structures; clever computing in development acceptance; clever computing in picture processing; clever computing in computing device imaginative and prescient; biometrics with functions to person security/forensic sciences; modeling, concept, and purposes of optimistic structures; sparse manifold studying equipment and purposes; advances in clever info processing.

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Extra resources 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

Example text

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]. However, to the best of our knowledge, there is no research work about the EDA for solving the FJSP.

The solution of the FJSP is a combination of operation scheduling decisions and machine assignment. So, a solution can be expressed by the processing sequence of operations and the assignment of operations on the machines. , operation sequence vector and machine assignment vector. Machine assignment vector Operation sequence vector 3 2 3 4 2 4 1 1 4 2 4 1 Job 1 1 4 Job 2 3 2 3 1 Job 3 3 2 Job 4 Fig. 1. Illustration of the representation of a feasible solution For the operation sequence vector, the number of genes equals to the total number of operations To .

Moreover, it can be seen that the average running time of EDA is acceptable, even for relatively larger-scale instances. So, the conclusion is that our EDA is effective and robust in solving the FJSP. 16 6 S. Wang et al. Conclusion This was the first report work to apply EDA for solving the FJSP. We designed a probability model with the superior population for the EDA to solve the FJSP by generating new individuals via sampling based on the probability model. With an updating mechanism for the probability model and local search based on critical path, the EDA was effective and efficient in solving the FJSP, which was demonstrated by simulation results and comparisons.

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