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 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 amassed in CCIS fifty one. ISICA meetings are one of many first sequence of foreign meetings on computational intelligence that mix components of studying, model, evolution and fuzzy good judgment to create courses as replacement strategies to synthetic intelligence.

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One way to tackle this problem is to import stronger global optimization techniques, such as genetic algorithms and others. In order to see the effect of genetic algorithm, we delete the PSFM optimization step, and run the program on the same datasets. Fig. 4 gives the result, and compared to that of Motif GibbsGA. 05 0 nSn nPPV nPC nCC sSn sSp sASP Fig. 4. Comparison of using or not using genetic algorithm 38 L. Liu and L. Jiao 4 Discussion We have developed a novel de novo motif identification approach based on Gibbs sampling coupled with PSFM optimization by genetic algorithm, which we implemented in our software Motif GibbsGA.

Improper disposal of medical waste may pose a significant risk to human health and the environment. Many investigations and studies have suggested that many proper waste management procedures are still not being well implemented[3,4,5]. Nowadays, some studies have focused on the practicability of applying the concept of reverse logistics to the medical waste management, which may greatly improve the efficiency of medical waste management and reduce the negative influence it may impose on the environment.

An individual can only compete and mate with its neighbors, but since the neighborhoods overlap good solutions may disseminate across the entire population. 26 S. Zhang and Z. He Finally, a few researchers have tried to combine two or more parallel methods to parallelize GAs, producing hierarchical parallel GAs. Hierarchical implementations can reduce the execution time more than any of their components alone. The speed-up of hierarchical PGAs is product of speed-up of one PGAs at higher level and speed-up of another PGAs at lower level.

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