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 foreign 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 information mining and data 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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Read Online or Download Advances in Computation and Intelligence: Third International Symposium, ISICA 2008 Wuhan, China, December 19-21, 2008 Proceedings PDF

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

Sample text

IEEE Service Center, Piscataway (1995) 21. : The particle swarm explosion, stability and convergence in a multi-dimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–63 (2002) 22. : Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. Evolutionary programming 7, 601–610 (1998) 23. : Evolutionary programming using the mutations based on the Levy probability distribution. cn Abstract. This paper introduced a dynamic-clone and chaos-mutation evolutionary algorithm (DCCM-EA), which employs dynamic clone and chaos mutation methods, for function optimization.

Where m is the niche size. To solve dynamic function optimization problems, we use evolutionary algorithm with niche strategy which producing a set of niches by mutation, that means Dynamic Searching in Sub-Spaces, so we call this new algorithm DSSSEA. In DSSSEA, firstly the individuals are ordered according to their fitness values, then around the individuals (in subspaces) producing niches by mutation, but the size of subspace is inversely proportional to the fitness of individual. The strategy is to speed up the convergence and to maintain the diversity to avoid local optima and premature.

In DSSSEA, firstly the individuals are ordered according to their fitness values, then around the individuals (in subspaces) producing niches by mutation, but the size of subspace is inversely proportional to the fitness of individual. The strategy is to speed up the convergence and to maintain the diversity to avoid local optima and premature. This algorithm only uses objective fitness values without the limit of continuous function, so it can be used in many kinds of problems. A multi-processor system with shared memory can be used to perform an asynchronous parallel algorithm efficiently.

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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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