The level set method is commonly used to address image noise removal. Existing studies concentrate mainly on determining the speed function of the evolution equation. Based on the idea of a Canny operator, this letter...
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The level set method is commonly used to address image noise removal. Existing studies concentrate mainly on determining the speed function of the evolution equation. Based on the idea of a Canny operator, this letter introduces a new method of controlling the level set evolution, in which the edge strength is taken into account in choosing curvature flows for the speed function and the normal to edge direction is used to orient the diffusion of the moving interface. The addition of an energy term to penalize the irregularity allows for better preservation of local edge information. In contrast with previous Canny-based level set methods that usually adopt a two-stage framework, the proposed algorithm can execute all the above operations in one process during noise removal.
Our new evolutionary method allows electronic commerce (EC) services on distinct distribution channels. Launching EC services on the Internet require careful on mobile agents. It supports EC transition flows written i...
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This paper describes a project funded by the European Commission which seeks to provide the technology and software infrastructure necessary to support the next generation of evolving infohabitants in a way that makes...
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Quantum-Inspired evolutionary Algorithm (QEA) is proposed as one of approximate algorithms to solve combinatorial optimization. QEA is evolutionary computation that uses quantum bits and suerposition states in quantum...
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A novel simulation-optimization framework is proposed that enables the automation of the hybrid stochastic modeling process for synthetic generation of multi-season streamflows. This framework aims to minimize the dru...
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A novel simulation-optimization framework is proposed that enables the automation of the hybrid stochastic modeling process for synthetic generation of multi-season streamflows. This framework aims to minimize the drudgery, judgment and subjectivity involved in the selection of the most appropriate hybrid stochastic model. It consists of a multi-objective optimization model as the driver and the hybrid multi-season stochastic streamflow generation model, hybrid matched block boostrap (HMABB) as the simulation engine. For the estimation of the hybrid model parameters, the proposed framework employs objective functions that aim to minimize the overall errors in the preservation of storage capacities at various demand levels, unlike the traditional approaches that are simulation based. Moreover this framework yields a number of competent hybrid stochastic models in a single run of the simulation-optimization framework. The efficacy of the proposed simulation-optimization framework is brought out through application to two monthly streamflow data sets from USA of varying sample sizes that exhibit multi-modality and a complex dependence structure. The results show that the hybrid models obtained from the proposed framework are able to preserve the statistical characteristics as well as the storage characteristics better than the simulation based HMABB model, while minimizing the manual effort and the subjectivity involved in the modeling process. The proposed framework can be easily extended to model multi-site multi-season streamflow data. (C) 2011 Elsevier B.V. All rights reserved.
Surface wave dispersion analysis is widely used in geophysics to infer near-surface shear (S)-wave velocity profiles for a wide variety of applications. However, inversion of surface wave data is challenging for most ...
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Surface wave dispersion analysis is widely used in geophysics to infer near-surface shear (S)-wave velocity profiles for a wide variety of applications. However, inversion of surface wave data is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this work, we proposed and implemented a new Rayleigh wave dispersion curve inversion scheme based on backtracking search algorithm (BSA), a novel and powerful evolutionary algorithm (EA). Development of BSA is motivated by studies that attempt to develop an algorithm that possesses desirable features for different optimization problems which include the ability to reach a problem's global minimum more quickly and successfully with a small number of control parameters and low computational cost, as well as robustness and ease of application to different problem models. The proposed inverse procedure is applied to nonlinear inversion of fundamental-mode Rayleigh wave dispersion curves for near-surface S-wave velocity profiles. To evaluate calculation efficiency and effectiveness of BSA, four noise-free and four noisy synthetic data sets are firstly inverted. Then, the performance of BSA is compared with that of genetic algorithms (GA) by two noise-free synthetic data sets. Finally, a real-world example from a waste disposal site in NE Italy is inverted to examine the applicability and robustness of the proposed approach on real surface wave data. Furthermore, the performance of BSA is compared against that of GA by real data to further evaluate scores of BSA. Results from both synthetic and actual data demonstrate that BSA applied to nonlinear inversion of surface wave data should be considered good not only in terms of the accuracy but also in terms of the convergence speed. The great advantages of BSA are that the algorithm is simple, robust and easy to implement. Also there are fewer control parameters to tune. (C) 2015 Elsevier B.V. All rights reserved.
Identifying coherent sub-graphs in networks is important in many applications. In power systems, large systems are divided into areas and zones to aid in planning and control applications. But not every partitioning i...
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Identifying coherent sub-graphs in networks is important in many applications. In power systems, large systems are divided into areas and zones to aid in planning and control applications. But not every partitioning is equally good for all applications;different applications have different goals, or attributes, against which solutions should be evaluated. This paper presents a hybrid method that combines a conventional graph partitioning algorithm with an evolutionary algorithm to partition a power network to optimize a multi-attribute objective function based on electrical distances, cluster sizes, the number of clusters, and cluster connectedness. Results for the IEEE RTS-96 show that clusters produced by this method can be used to identify buses with dynamically coherent voltage angles, without the need for dynamic simulation. Application of the method to the IEEE 118-bus and a 2383-bus case indicates that when a network is well partitioned into zones, intra-zone transactions have less impact on power flows outside of the zone;i.e., good partitioning reduces loop flows. This property is particularly useful for power system applications where ensuring deliverability is important, such as transmission planning or determination of synchronous reserve zones.
Gene Expression Programming is a new evolutionary algorithm that overcomes many limitations of the more established Genetic algorithms and Genetic Programming. Its application to event selection in high energy physics...
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Gene Expression Programming is a new evolutionary algorithm that overcomes many limitations of the more established Genetic algorithms and Genetic Programming. Its application to event selection in high energy physics data analysis is presented using as an example application the selection of K-S particles produced in e(+)e(-) interactions at 10 GeV and reconstructed in the decay mode K-S -> pi(+)pi(-). The algorithm was used for automatic identification of classification criteria for signallbackground separation. For the problem studied and for data samples with signal to background ratios between 0.25 and 5, the classification accuracy obtained with the criteria developed by the GEP algorithm was in the range of 92-95%. (C) 2007 Elsevier B.V. All rights reserved.
A challenge in hybrid evolutionary algorithms is to define efficient strategies to cover all search space, applying local search only in actually promising search areas. This paper proposes a way of detecting promisin...
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The multiple sequence alignment problem (MSA) can be reformulated as the problem of finding a maximum weight trace in an alignment graph, which is derived from all pairwise alignments. We improve the alignment graph b...
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