Download e-book for kindle: Applications of Evolutionary Computing: EvoWorkshops 2009: by Jaroslaw Skaruz, Franciszek Seredynski (auth.), Mario

By Jaroslaw Skaruz, Franciszek Seredynski (auth.), Mario Giacobini, Anthony Brabazon, Stefano Cagnoni, Gianni A. Di Caro, Anikó Ekárt, Anna Isabel Esparcia-Alcázar, Muddassar Farooq, Andreas Fink, Penousal Machado (eds.)

ISBN-10: 3642011284

ISBN-13: 9783642011283

ISBN-10: 3642011292

ISBN-13: 9783642011290

This ebook constitutes the refereed joint court cases of 11 ecu workshops at the idea and functions of Evolutionary Computation, EvoWorkshops 2009, held in Tübingen, Germany, in April 2009 in the scope of the EvoStar 2009 event.

The sixty eight revised complete papers and 23 revised brief papers offered have been conscientiously reviewed and chosen from a complete of 143 submissions. With appreciate to the 11 workshops coated, the papers are equipped in topical sections on telecommunication networks and different parallel and disbursed platforms, environmental concerns, finance and economics, video games, layout automation, picture research and sign processing, interactive evolution and humanized computational intelligence, tune, sound, paintings and layout, non-stop parameter optimisation, stochastic and dynamic environments, in addition to transportation and logistics.

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Read or Download Applications of Evolutionary Computing: EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG, Tübingen, Germany, April 15-17, 2009. Proceedings PDF

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Extra resources for Applications of Evolutionary Computing: EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG, Tübingen, Germany, April 15-17, 2009. Proceedings

Sample text

From the results in Table 3, the phase II search gets better results than the phase I overall. However, the average location error obtained using phase I has a tighter upper bound, which means that phase II will ocasionally obtain much larger location errors (as in instance 3-19C). A more detailed observation on the results confirms it. Phase I, which uses the error norm function as fitness function obtains a location error generally ranging from 1 to 10 meters. Phase II, which uses the maximum likelihood criterion, gets solutions that are either highly accurate (approximately 10 cm location error), or grossly inaccurate (from 10 to 30 m).

It reduces the location error obtained with phase I by more than 30% in six instances, and in five instances compared to phase II, with two other instances having an improvement over 20%. On the negative side, for instances 3-19D and 3-25B the two-stage process is largely outperformed by both single phases. It appears that for these instances, the phase II performs best; the poor results obtained with the proposed method might be caused by a poor balance between the two stages; increasing the weight of the second phase might improve the results.

The basic intuition is to use each function where it performs best. Thus, in the first phase L1 is used starting from a random initial solution, then in the second phase the output is refined using ML. More details on this can be found in Section 5. 5 Experiments and Results We selected 10 measurement sets as problem instances, and compiled the error model from the 23 remaining sets, so that no information from an instance is used in the process of resolving it. The number of beacons is set to 10% of the total number of nodes, the beacons are randomly selected for each run.

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Applications of Evolutionary Computing: EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG, Tübingen, Germany, April 15-17, 2009. Proceedings by Jaroslaw Skaruz, Franciszek Seredynski (auth.), Mario Giacobini, Anthony Brabazon, Stefano Cagnoni, Gianni A. Di Caro, Anikó Ekárt, Anna Isabel Esparcia-Alcázar, Muddassar Farooq, Andreas Fink, Penousal Machado (eds.)


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