Download e-book for kindle: Advances in Computation and Intelligence: Third by Yong Liu (auth.), Lishan Kang, Zhihua Cai, Xuesong Yan, Yong

By Yong Liu (auth.), Lishan Kang, Zhihua Cai, Xuesong Yan, Yong Liu (eds.)

ISBN-10: 3540921362

ISBN-13: 9783540921363

ISBN-10: 3540921370

ISBN-13: 9783540921370

This publication constitutes the refereed lawsuits of the 3rd overseas Symposium on Intelligence Computation and functions, ISICA 2008, held in Wuhan, China, in December 2008.

The ninety three revised complete papers have been rigorously reviewed and chosen from approximately seven-hundred submissions. The papers are geared up in topical sections on computational intelligence, evolutionary computation, evolutionary multi-objective and dynamic optimization, evolutionary studying platforms, neural networks, type and popularity, bioinformatics and bioengineering, evolutionary facts mining and information discovery, clever GIS and keep an eye on, thought of clever computation, combinational and numerical optimization, in addition to real-world applications.

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Additional resources for Advances in Computation and Intelligence: Third International Symposium, ISICA 2008 Wuhan, China, December 19-21, 2008 Proceedings

Example text

The results of this comparison are shown in Table 3 with respect to the mean best values found and the standard deviations for each function between DCCM-EA and FEP. From the results, it can be seen that the results of DCCM-EA is not better than FEP for f3 and is little worse than FEP for f15, but it is Table 2. The best, worst and mean best values for each function got by DCCM-EA. Each functoin is evaluated independently for 50 runs. 53640 26 M. Yang and J. Guan Table 3. Comparison between DCCM-EA and FEP, where “Mean Best” and “Std Dev” stand for the mean best values and the standard deviation respectively.

As a result, the promising areas are gained quickly which decrease useless search greatly. In fact, the second local search operator is a powell algorithm which can find the local optimal solution near the start point[11]. When the algorithm run, the multi-parent crossover operator can enables individual to draw closer to each local optimal solution, thus the population will be divided into subpopulations automatically,meanwhile, the first local search operator is adopted to gain near-optimal solution in each subpopulation.

Journal of Global Optimization 11, 341–359 (1997) 11. : Mixed Variable Non-linear Optimization by Differential Evolution. In: Proceedings of Nostradamus 1999 2nd International Prediction Conference, pp. 45–55 (1999) 12. : Differential Evolution-A Practical Approach to Global Optimization. Springer, Heidelberg (2005) 13. : Chaos and Fractals. Springer, New York (1992) 14. : Global Optimization. LNCS, vol. 350. Springer, Heidelberg (1989) 15. : Evolution and Optimum Seeking. com Abstract. In this paper, a parallel self-adaptive subspace searching algorithm is proposed for solving dynamic function optimization problems.

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Advances in Computation and Intelligence: Third International Symposium, ISICA 2008 Wuhan, China, December 19-21, 2008 Proceedings by Yong Liu (auth.), Lishan Kang, Zhihua Cai, Xuesong Yan, Yong Liu (eds.)


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