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 publication constitutes the refereed joint complaints of 11 eu workshops at the conception 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 awarded have been rigorously reviewed and chosen from a complete of 143 submissions. With recognize to the 11 workshops coated, the papers are equipped in topical sections on telecommunication networks and different parallel and allotted structures, environmental concerns, finance and economics, video games, layout automation, photograph research and sign processing, interactive evolution and humanized computational intelligence, song, sound, paintings and layout, non-stop parameter optimisation, stochastic and dynamic environments, in addition to transportation and logistics.

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Additional info 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

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4 J. Skaruz and F. Seredynski of an individual. There is a simple method used for translation from genotype to phenotype and inversely. The advantage of distinction between genotype and phenotype is that after any genetic change of a genome ET is always correct and solution space can be searched through in a more extent. At the beginning chromosomes are generated randomly. Next, in each iteration of GEP, a linear chromosome is expressed in the form of ET and executed. The fitness value is calculated and termination condition is checked.

Effect of Robot Speed. 35 m/s. 75 m/s. The total number of robots is fixed to 30. The results for increasing movement speed are shown in Figure 3. It is interesting to see that the speed of the robots has little influence on the performance. For Follow route there is a small increase in the robot travel time, but no noticeable effect on the traveled distance. For Follow estimate, there is no noticeable effect for either measure. The higher vulnerability of the Follow route behavior is to be expected, as high node mobility leads to higher variability and more frequent disconnections of the LoS MANET route, so that it becomes difficult to follow it hop by hop.

Acknowledgment This work was supported by the Ministry of Science and Higher Education under grant no. N N519 319935. 10 J. Skaruz and F. Seredynski References 1. : Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems 13(2), 87–129 (2001) 2. : Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Angra do Heroismo, Portugal (2002) 3. : Gene Expression Programming and the Evolution of Computer Programs. J. ) Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Appraoches (Recent Developments in Biologically Inspired Computing).

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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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