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By Doina Bucur, Giovanni Iacca, Giovanni Squillero, Alberto Tonda (auth.), Anna I. Esparcia-Alcázar (eds.)

This booklet constitutes the refereed lawsuits of the overseas convention at the purposes of Evolutionary Computation, EvoApplications 2013, held in Vienna, Austria, in April 2013, colocated with the Evo* 2013 occasions EuroGP, EvoCOP, EvoBIO, and EvoMUSART. The sixty five revised complete papers offered have been rigorously reviewed and chosen from 119 submissions. EvoApplications 2013 consisted of the subsequent 12 tracks: EvoCOMNET (nature-inspired strategies for telecommunication networks and different parallel and disbursed systems), EvoCOMPLEX (evolutionary algorithms and complicated systems), EvoENERGY (evolutionary computation in strength applications), EvoFIN (evolutionary and normal computation in finance and economics), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in photo research, sign processing, and trend recognition), EvoINDUSTRY (nature-inspired concepts in business settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for possibility administration, safeguard and defence applications), EvoROBOT (evolutionary computation in robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).

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Additional info for Applications of Evolutionary Computation: 16th European Conference, EvoApplications 2013, Vienna, Austria, April 3-5, 2013. Proceedings

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SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009) 7. : A Novel Generic Graph Model for Traffic Grooming in Heterogeneous WDM Mesh Networks. IEEE/ACM Transaction on Networking 11, 285–299 (2003) 8. : A Review of Traffic Grooming in WDM Optical Networks: Architectures and Challenges. Optical Networks Magazine 4(2), 55–64 (2003) 9. : Traffic Grooming in an Optical WDM Mesh Network. IEEE Journal on Selected Areas in Communications 20(1), 122–133 (2002) 10. : Comparison of Multiobjective Evolutionary Algorithms: Empirical Results.

In addition to the convergence time other performance metrics were assessed. Due to the unavailability of the actual Pareto front for this problem, the spacing indicator SP (S) has been used, which measures the uniform distribution of solution but which does not say anything about the number of solutions over the Pareto front, or its coverage. The formulation of the spacing index is reported in the following equations (Eq. 9 and 10). SP (S) = di = min 1/|S − 1| sk ∈S∩sk =si M m=1 |S| i=1 di − d , |fm (si ) − fm (sk )| .

At each node of N , the number of incoming physical links will be identical to the number of outgoing physical links. – The number of transceivers (Ti /Ri or simply T ) will be greater than or equal to 1, for all nodes in N . – None of the nodes in N supports the wavelength conversion facility. Thus, we assume the wavelength continuity constraint [3]: all lightpaths must use the same wavelength (λ) across all physical links that they traverse. – We suppose a static traffic pattern, where the set of low-speed traffic requests are known in advance.

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