Home Nonfiction 7 • Applications of Evolutionary Computation: EvoApplications by Uğur Akyazı, A. Şima Uyar (auth.), Cecilia Di Chio, Anthony PDF

Applications of Evolutionary Computation: EvoApplications by Uğur Akyazı, A. Şima Uyar (auth.), Cecilia Di Chio, Anthony PDF

By Uğur Akyazı, A. Şima Uyar (auth.), Cecilia Di Chio, Anthony Brabazon, Gianni A. Di Caro, Marc Ebner, Muddassar Farooq, Andreas Fink, Jörn Grahl, Gary Greenfield, Penousal Machado, Michael O’Neill, Ernesto Tarantino, Neil Urquhart (eds.)

ISBN-10: 3642122418

ISBN-13: 9783642122415

ISBN-10: 3642122426

ISBN-13: 9783642122422

Evolutionary computation (EC) strategies are e?cient, nature-inspired me- ods according to the foundations of usual evolution and genetics. as a result of their - ciency and easy underlying ideas, those tools can be utilized for a various rangeofactivitiesincludingproblemsolving,optimization,machinelearningand trend reputation. a wide and regularly expanding variety of researchers and pros utilize EC ideas in a number of software domain names. This quantity provides a cautious collection of proper EC examples mixed with a radical exam of the suggestions utilized in EC. The papers within the quantity illustrate the present cutting-edge within the program of EC and may support and encourage researchers and pros to advance e?cient EC tools for layout and challenge fixing. All papers during this ebook have been offered in the course of EvoApplications 2010, which incorporated quite a number occasions on application-oriented facets of EC. on the grounds that 1998, EvoApplications — previously referred to as EvoWorkshops — has supplied a different chance for EC researchers to satisfy and talk about software elements of EC and has been an enormous hyperlink among EC examine and its program in a number of domain names. in the course of those 12 years, new occasions have arisen, a few have disappeared,whileothershavematuredtobecomeconferencesoftheirown,such as EuroGP in 2000, EvoCOP in 2004, and EvoBIO in 2007. And from this 12 months, EvoApplications has develop into a convention as well.

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Additional info for Applications of Evolutionary Computation: EvoApplications 2010: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoMUSART, and EvoTRANSLOG, Istanbul, Turkey, April 7-9, 2010, Proceedings, Part II

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Once training of the ANNP is accomplished, the trained ANNP can operate in the performance mode (phase). In performance (testing) phase, the ANNP is expected to generalize, that is to respond to inputs it has never been seen before, but drawn from the same distribution as the inputs used in the training set. One way of explaining the generalization exhibited by the network during the performance phase is by remembering that after the training phase is complete the ANNP has established an approximation of the desired input/output mapping; hence, during the performance phase the ANNP produces outputs to previously unseen inputs by interpolation between the inputs used (seen) in the training phase.

In addition, signals of arbitrary angles of arrival could be tracked with negligible errors. 18 M. M. B. Ayoob Fig. 3. Simulated real time AAA system pattern. Test signals: θ1 = -22°, θ2 = -8°, θ3 =8°, θ4 = 22°, ∆θ=15° and 4 dB SNR. Blue and green represent the true and test response respectively. Fig. 4. Simulated real time AAA system pattern. Test signals: θ1= -30°, θ2 = -40°, θ3 = -55°, θ4 = -70°, ∆θ=15°, and 4dB SNR. Blue and green represent the true and test response respectively. Fig. 3 shows a beam pattern when angular separation between test sources is 12° and their SNR is 4 dB, although the training was performed on sources of ∆θ = 10°.

This reflects the capability of the system to track signals it has never seen and in a sector of different sizes. Performance Evaluation of an Artificial Neural Network-Based AAA System 19 Fig. 5. Simulated real time AAA system pattern. Traning enviroment:( -45° – 45°) sector, ∆θ=2°,4°, 6°,8°,10°, and 5dB SNR. Test signals: θ1 = -29°, θ2 = -20°, θ3 = 12°, θ4 = 4°, θ5 = -4°, θ6 = -12°, θ7 = -21°, θ8 = -28°, ∆θ= 8°, and 4dB SNR. Blue and green represent the true and test responses. 5, eight test signals existed in the (-45° – 45°) sector with an angular separation of 9° tracked perfectly with lower SNR than that trained on.

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Applications of Evolutionary Computation: EvoApplications 2010: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoMUSART, and EvoTRANSLOG, Istanbul, Turkey, April 7-9, 2010, Proceedings, Part II by Uğur Akyazı, A. Şima Uyar (auth.), Cecilia Di Chio, Anthony Brabazon, Gianni A. Di Caro, Marc Ebner, Muddassar Farooq, Andreas Fink, Jörn Grahl, Gary Greenfield, Penousal Machado, Michael O’Neill, Ernesto Tarantino, Neil Urquhart (eds.)


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