Applications of Evolutionary Computation: EvoApplications by Farhan Ahammed, Pablo Moscato (auth.), Cecilia Di Chio,

By Farhan Ahammed, Pablo Moscato (auth.), Cecilia Di Chio, Stefano Cagnoni, Carlos Cotta, Marc Ebner, Anikó Ekárt, Anna I. Esparcia-Alcázar, Juan J. Merelo, Ferrante Neri, Mike Preuss, Hendrik Richter, Julian Togelius, Georgios N. Yannakakis (eds.)

This ebook constitutes the refereed complaints of the foreign convention at the purposes of Evolutionary Computation, EvoApplications 2011, held in Torino, Italy, in April 2011 colocated with the Evo* 2011 occasions. due to the massive variety of submissions got, the court cases for EvoApplications 2011 are divided throughout volumes (LNCS 6624 and 6625). the current quantity includes contributions for EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC. The 36 revised complete papers awarded have been rigorously reviewed and chosen from various submissions. This quantity provides an summary in regards to the newest examine in EC. components the place evolutionary computation ideas were utilized diversity from telecommunication networks to advanced platforms, finance and economics, video games, photograph research, evolutionary song and artwork, parameter optimization, scheduling, and logistics. those papers may supply guidance to aid new researchers tackling their very own challenge utilizing EC.

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Additional resources for Applications of Evolutionary Computation: EvoApplications 2011: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, Torino, Italy, April 27-29, 2011, Proceedings, Part I

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We recall that chaotic networks have chaotic neighbours (see Section 5), therefore local search might not escape from that region and get stuck until time runs out.

The report details a broad, multi-disciplinary research agenda for developing the ultra-large-scale systems of the future. Here we focus on computer-supported evolution, adaptable structure and emergent quality, that can be placed under the umbrella of Autonomic computing (AC). AC is based on the assumption that the increasing complexity of distributed systems is becoming a limiting factor for further development. The solution proposed by the AC research community is to provide systems with four key properties: self-configuration, self-healing, self-optimization, self-protection [10].

The evolutionary algorithm tries to evolve instances of a given problem, such that the algorithm (or its software implementation) requires a lot of steps (time) to produce the correct answer. g. Bubble Sort and Shell Sort) which have already been studied in depth theoretically and experimentally. They found that for any problem of finite-size, their analysis was able to provide a useful lower-bound on the worst-case complexity of the algorithms they analysed and that possibly this mixed evolutionary-statistical analysis can provide a positive contribution, when other approaches of analysing algorithms constitute a hard task for the researcher.

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