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.)

ISBN-10: 3642205240

ISBN-13: 9783642205248

ISBN-10: 3642205259

ISBN-13: 9783642205255

This publication 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. because of the big variety of submissions got, the court cases for EvoApplications 2011 are divided throughout volumes (LNCS 6624 and 6625). the current quantity comprises contributions for EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC. The 36 revised complete papers provided have been conscientiously reviewed and chosen from a variety of submissions. This quantity provides an summary in regards to the newest study in EC. parts the place evolutionary computation strategies were utilized variety from telecommunication networks to advanced structures, finance and economics, video games, snapshot research, evolutionary tune and artwork, parameter optimization, scheduling, and logistics. those papers may supply instructions to assist new researchers tackling their very own challenge utilizing EC.

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Extra 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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Evolving L-Systems as an Intelligent Design Approach to Find Classes 7 Algorithm 1. Basic structure of the algorithm used to modify a fractal to generate more difficult instances. 1 L ← Original L-System (base case) 2 for every μ-combination of ‘F ’ in L do 3 for i = 1 to ξ do 4 Replace each chosen ‘F ’ with the sequence in Equation (2) 5 Li ← Modified L-System 6 Use Equation (1) to compute fitness of Li relative to L 7 if f (Fi ) > f (F ) then 8 Save Fi to memory/file 9 end 10 if Not enough samples produced difficult-to-solve instances then 11 Move on to next combination 12 end 13 end 14 end 5 Simulation Results The local search optimization algorithm was run on a computer running a 32bit Linux OS with two 3GHz processors and 2GB of ram.

The peer may own the requested resource, or may propagate the request to other peers. It is meaningful to say that these peers 18 M. Amoretti are indirectly affected by the environments input. When the system receives an input, not all its nodes must be considered for evolution, but only those which are directly or indirectly affected by the environments input. Thus, to design τ plans at the level of single peer is a reasonable tradeoff. g. of its k neighbors. Genetic algorithms (GA), introduced by John Holland in 1975, have been the first phylogenetic evolutionary computing paradigm to be developed and applied [9].

2 Overview of Boolean Networks BNs have been firstly introduced by Kauffman [12] and subsequently received considerable attention in the composite community of complex systems research. A BN is a dynamical system whose state at time t ∈ N is defined by a binary vector s(t) = (x1 (t), . . , xN (t)) of size N , in which xi (t) ∈ {0, 1}. State transitions are defined as s(t + 1) = (x1 (t + 1), . . , xN (t + 1)), where xi (t + 1) = fi (xi1 , . . , xiKi ) and Ki is the number of arguments of function fi .

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


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