New PDF release: Advances in Computation and Intelligence: 4th International

By Fengrui Wang, Wenhong Wang, Quanke Pan, Fengchao Zuo (auth.), Zhihua Cai, Zhenhua Li, Zhuo Kang, Yong Liu (eds.)

ISBN-10: 3642048420

ISBN-13: 9783642048425

ISBN-10: 3642048439

ISBN-13: 9783642048432

Volumes CCIS fifty one and LNCS 5812 represent the lawsuits 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 studies, 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, version, evolution and fuzzy common sense to create courses as substitute options to man made intelligence.

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Additional info for Advances in Computation and Intelligence: 4th International Symposium, ISICA 2009 Huangshi, China, Ocotober 23-25, 2009 Proceedings

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In each gene we defined time duration in which one technical indicator should be traced. And each technical indicator’s weight in the decision was also defined. The weighting will be adjusted during the evolve progress. 2 Genetic Algorithm In predicting process each individual analyzes a period time financial data from market using these technical indicators. Each technical indicator gives signal of rise, down of stock price according analysis theory[4]. Different analysis time scales were used to calculate these technical indicators.

The 0order background model is B0 = [qA,0, qC,0 , qG,0 , qT ,0 ] , this means that each base is drawn independently from a single discrete distribution. The m-order background model is Bm, this means that the probability of finding a certain nucleotide in a sequence depends on the m previous nucleotides in the sequence. For a segment s in a sequence, the probability Ps of segment s being generated by the background model Bm is given by Moitf GibbsGA: Sampling Transcription Factor Binding Sites Ps = P(s1 LsW | Bm ) = ∏i =1 P(si | si−1,L, si−m ) W 33 (2) Corresponding to PSFM, the position weight matrix (PWM) is defined as M W , each entry in PWM is mb , j = ln( pb , j / qb ,0 ) .

McGraw-Hill Trade (2000) Implementation of Parallel Genetic Algorithm Based on CUDA Sifa Zhang and Zhenming He Abstract. Genetic Algorithm (GA) is a powerful tool for science computing, while Parallel Genetic Algorithm (PGA) further promotes the performance of computing. However, the traditional parallel computing environment is very difficult to set up, much less the price. This gives rise to the appearance of moving dense computing to graphics hardware, which is inexpensive and more powerful.

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Advances in Computation and Intelligence: 4th International Symposium, ISICA 2009 Huangshi, China, Ocotober 23-25, 2009 Proceedings by Fengrui Wang, Wenhong Wang, Quanke Pan, Fengchao Zuo (auth.), Zhihua Cai, Zhenhua Li, Zhuo Kang, Yong Liu (eds.)


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