A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exp...
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A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.
Aiming at the dynamic policy trigger problem in policy based network management, this thesis first introduces the uncertain information generated by onepoint coverage random sets theory and also the unified framework ...
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IEEE802.16, as a wireless MAN broadband access standard, defines a flexible QoS mechanism in MAC layer for wild variety classes of services. However, it dose not define specific admission control strategy and scheduli...
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In this paper, we mainly discuss the relationship between the extended Pawlak flow graph (EFG) with granular computing (GrC), and develop a both simple and concrete model for EFG using GrC. The distinct advantage is t...
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This paper researched the cooperating game in logistics outsourcing under asymmetric information. It set up the cooperative game model in condition of TPL buyer can not observe the TPL logistics operation cost and man...
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The E-commerce information on the Surface Web is supported by the Deep Web, which can not be accessed directly by the search engines or the web crawlers. The only way to access the backend database is through query in...
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A novel dynamic software watermark scheme based on the Shamir threshold and branch structure is presented. First, we split the watermark into a set of shares using the Shamir threshold scheme. Second, these values are...
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A novel dynamic software watermark scheme based on the Shamir threshold and branch structure is presented. First, we split the watermark into a set of shares using the Shamir threshold scheme. Second, these values are encrypted with the DES block cipher that forms the watermark shares to be embedded into different methods of program according to the dynamic behavior of the branch structure. Our scheme can withstand most semantics-preserving attacks and can retrieve the original watermark based on partial information. simulation tests show that our scheme is very robust, stealthy and has a high price performance rate compared with other methods.
The watermark is split into a set of shares and represented as a set of graphs, which are embedded into the program dynamically. Code obfuscating technique in the embedding phase was used. A novel scheme of dynamic da...
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The watermark is split into a set of shares and represented as a set of graphs, which are embedded into the program dynamically. Code obfuscating technique in the embedding phase was used. A novel scheme of dynamic data structure software watermarking is presented. This scheme can restore the watermark according to partial watermark shares and is very robust and stealthy.
In this paper, we apply the least-square support vector machine (LS-SVM) to operon prediction of Escherichia coli (***), with different combinations of intergenic distance, gene expression data, and phylogenetic profi...
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ISBN:
(纸本)9781424440085
In this paper, we apply the least-square support vector machine (LS-SVM) to operon prediction of Escherichia coli (***), with different combinations of intergenic distance, gene expression data, and phylogenetic profile. Experimental results demonstrate that the WO pairs tend to have shorter intergenic distances, higher correlation coefficient and much stronger relation of co-envoled between phylogenetic profiles. Also, we dealt with the data sets extracted from WOs¿ and TUBs¿, processed the intergenic distances with log-energy entropy, de-noised the Pearson correlation coefficients of two genes expression data with wavelet transform, and computed the Hamming distances of two phylogenetic profiles. Then we trained LS-SVM using part of the data sets and tested the trained classifier model using the rest data sets. It shows that different combinations of features could affect the prediction results. When the combination of intergenic distance, gene expression data and phylogenetic profile is taken as the input of LS-SVM in the linear kernel type, good results can be obtained, of which the accuracy, sensitivity and specificity are 92.34%, 93.54%, and 90.73%, respectively.
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