By Fengrui Wang, Wenhong Wang, Quanke Pan, Fengchao Zuo (auth.), Zhihua Cai, Zhenhua Li, Zhuo Kang, Yong Liu (eds.)
Volumes CCIS fifty one and LNCS 5812 represent the court cases of the Fourth Interational Symposium on Intelligence Computation and functions, ISICA 2009, held in Huangshi, China, in the course of October 23-25.
ISICA 2009 attracted over three hundred submissions. via rigorous experiences, fifty eight papers have been incorporated in LNCS 5821,and fifty four papers have been accumulated in CCIS fifty one. ISICA meetings are one of many first sequence of foreign meetings on computational intelligence that mix components of studying, variation, evolution and fuzzy good judgment to create courses as replacement strategies to man made intelligence.
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Such algorithms assume that several subpopulations (demes) evolve in parallel and demes are relatively isolated. It has two important characteristics: (1) relatively large subpopulations, (2) migration, which allows each subpopulation exchange individuals. Such a PGAs leaves us three issues: (1) find a migration rate that makes isolated demes behave like a single panmictic population, (2) find a suitable communication topology to increase the number of mixture and not increase too much communication cost, (3) find a optimal number of demes that maximizes quality of solution.
It means each block can access/write subpopulation independently, that is absolutely we have to do in parallelization. Although fine-grained GAs is suitable for multi-core processor computer, it only allows one population, and the limited interaction between individuals. Those are not quite match with the structure of thread in CUDA. However, grids separate each block like isolated subpopulation, according to Grosso’s conclusion in 1985, who conveyed that the quality of solution found after convergence was worse in the isolated case than in the single population, so we imports migration between blocks in order to improve quality of solution.
China 2. : Agent based genetic algorithm employing financial technical analysis for making trading decisions using historical equity market data. In: Proc. of the IEEE/WIC/ACM Int’l Conf. on Intelligent Agent Technology (IAT 2004), Beijing. China, pp. 421–424 (2004) 3. : Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, 2nd edn. MIT Press, Cambridge (1992) 4. : Technical Analysis from A to Z. McGraw-Hill Trade (2000) Implementation of Parallel Genetic Algorithm Based on CUDA Sifa Zhang and Zhenming He Abstract.