Get Applications of Evolutionary Computation: EvoApplications PDF

By Cecilia Di Chio, Anthony Brabazon, Marc Ebner, Muddassar Farooq, Andreas Fink, Jorn Grahl, Gary Greenfield, Penousal Machado, Michael O'Neill, Ernesto Tarantino, Neil Urquhart

ISBN-10: 3642122418

ISBN-13: 9783642122415

This booklet constitutes the refereed lawsuits of the foreign Workshops at the purposes of Evolutionary Computation, EvoApplications 2010, held in Istanbul, Turkey, in April 2010 colocated with the Evo* 2010 occasions. because of the massive variety of submissions acquired, the complaints for EvoApplications 2010 are divided throughout volumes (LNCS 6024 and 6025). the current quantity includes contributions for EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoMUSART, and EvoTRANSLOG. The forty seven revised complete papers provided have been rigorously reviewed and chosen from a complete of 86 submissions. This quantity offers a cautious choice of appropriate EC examples mixed with an intensive exam of the innovations utilized in EC. The papers within the quantity illustrate the present cutting-edge within the program of EC and will support and encourage researchers and pros to enhance effective EC tools for layout and challenge fixing.

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Additional info for Applications of Evolutionary Computation: EvoApplications 2010: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoMUSART, and EvoTRANSLOG, Istanbul, Turkey, April ... Computer Science and General Issues)

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B. Ayoob Superresolution algorithms for direction finding have been successfully applied to the problem of DOA estimation to locate radiating sources with additive noise, uncorrelated, and correlated signals. One of the main disadvantages of the superresolution algorithms is that they require extensive computation and as a result they are difficult to meet the real world applications requirements efficiently. Recently, neural networks-based direction finding algorithms have been proposed for single and multiple source direction finding [1], [4], [9], [11].

31. S. Linux Fedora core 6. Each one of these independent runs performs different vehicular mobility and communication patters based on independent random seeds inside each (ns-2 ) simulation, hence contributing to the generalization of the results. Therefore, a total of 30 different communication/mobility scenarios are analyzed for every optimization algorithm (we have 5 algorithms in our study). 1 Parameter Settings of the Optimization Algorithms All studied algorithms were configured in order to perform 5,000 solution evaluations per run.

Table 3 summarizes the parameter setting specific to each algorithm. Table 3. 8 Simulation Results and Comparisons Table 4 contains the results obtained after the experimentation. The second column contains both, the mean and the standard deviation (std) of the resulted best fitness values (out of 30 independent runs) for each one of the five optimization algorithms. In order to provide statistical meaningful comparisons, we have applied Friedman and Signed Ranked (Wilcoxon) statistical tests [19] to the numerical distributions of results.

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Applications of Evolutionary Computation: EvoApplications 2010: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoMUSART, and EvoTRANSLOG, Istanbul, Turkey, April ... Computer Science and General Issues) by Cecilia Di Chio, Anthony Brabazon, Marc Ebner, Muddassar Farooq, Andreas Fink, Jorn Grahl, Gary Greenfield, Penousal Machado, Michael O'Neill, Ernesto Tarantino, Neil Urquhart


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