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 complaints 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 stories, fifty eight papers have been integrated in LNCS 5821,and fifty four papers have been amassed in CCIS fifty one. ISICA meetings are one of many first sequence of foreign meetings on computational intelligence that mix components of studying, edition, evolution and fuzzy good judgment to create courses as substitute suggestions to man made intelligence.
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Additional resources for Advances in Computation and Intelligence: 4th International Symposium, ISICA 2009 Huangshi, China, Ocotober 23-25, 2009 Proceedings
The crossover operation exchanges two allele genes between two individuals. The successful prediction ratio is adopted as fitness. 3 Historical Market Data In this paper we use real-life stock market data to train and test the population. We select HuShen 300 index, which is an index listing the 300 selected companies registered in Shanghai Stock Exchange and Shenzhen Stock Exchange in China market. 20 H. Jiang and L. 1 Training For getting better predicting ability we trained the population in three stages.
Edu/assessment/. These datasets comprised of eukaryotic binding sites belonging to 52 transcription factors representing four different species, 6 belonging to fly, 26 belonging to human, 12 belonging to mouse and 8 belonging to yeast. The binding sites of each transcription factor were presented in three different background models, ‘real’, ‘generic’, and ‘Markov’. For each of the different type of background model, the known positions of the binding sites were kept unchanged. Four additional datasets of type ‘Markov’ containing no planted binding sites were included as negative controls.
Since there is a high-speed shared memory in each thread block, the communication cost is much less than traditional network. After analyzing the special architecture of CUDA, we proposed a Hierarchical PGAs, which is a product of multi-demes PGAs at higher level and master-slave PGAs at lower level. 4. The step of hierarchical PGAs: (1) CPU initializes a single large panmictic population in global memory; (2) Distribute subpopulation to each shared memory of thread block; (3) Each thread block starts evolution independently; (4) Tournament selection, crossover, mutation, evaluation of fitness; (5) Each block exchanges best individuals using island model; (6) Each grid exchanges best individuals using stepping stone model; (6) Update global memory with new individuals; (7) If objective function not satisfied, go to (2).