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

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

This ebook constitutes the completely refereed post-conference complaints of the seventh overseas convention on clever Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. The ninety four revised complete papers offered 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 photo processing and popularity; combinatorial and numerical optimization; computer studying concept and strategies; clever keep an eye on and automation; wisdom representation/reasoning and professional structures; clever computing in development popularity; clever computing in photograph processing; clever computing in laptop imaginative and prescient; biometrics with purposes to person security/forensic sciences; modeling, idea, and functions of optimistic structures; sparse manifold studying tools 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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An Effective Use of Crowding Distance in Multi-objective Particle Swarm Optimization. , pp. 257–264 (2005) 6. : A Hybrid Flow-Shop Scheduling Approach Based on Multi-Objective Particle Swarm Optimization. Computer Engineering and Science 8(31), 52–56 (2009) 7. : Baldwinian Learning in Clonal Selection Algorithm for Optimization. Information Science, 1218–1236 (2010) 8. : Particle Swarm Optimization. In: Proc. IEEE Int. Conf. Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995) 9. : Comparison of Multi-objective Evolutionary Algorithms: Empirical Results.

257–264 (2005) 6. : A Hybrid Flow-Shop Scheduling Approach Based on Multi-Objective Particle Swarm Optimization. Computer Engineering and Science 8(31), 52–56 (2009) 7. : Baldwinian Learning in Clonal Selection Algorithm for Optimization. Information Science, 1218–1236 (2010) 8. : Particle Swarm Optimization. In: Proc. IEEE Int. Conf. Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995) 9. : Comparison of Multi-objective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000) 10.

The termination condition of the Modified Inver-over operator is same as GSTM in reference [9]. The computation results are given in Table 2. 4580), Ave. 8231) and Ave. 0944). Hence, based on Table 2, it is concluded that the Modified Inver-over operator is clearly superior to the GSTM algorithm for the Euclidean traveling salesman problem. 22 Y. Wang et al. Table 2. Comparisons between Modified Inver-over operator and GSTM algorithm Modified Inver-over operator GSTM algorithm [9] Best Ave. (%) Ave.

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