Get Applications of Evolutionary Computation: 16th European PDF

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

This e-book constitutes the refereed complaints of the overseas convention at the functions 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 concepts for telecommunication networks and different parallel and dispensed systems), EvoCOMPLEX (evolutionary algorithms and complicated systems), EvoENERGY (evolutionary computation in power applications), EvoFIN (evolutionary and ordinary computation in finance and economics), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in picture research, sign processing, and trend recognition), EvoINDUSTRY (nature-inspired concepts in commercial settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for hazard administration, safety and defence applications), EvoROBOT (evolutionary computation in robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).

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

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1 Noise Analysis WSNs are affected by a number of stochastic effects, including radio-frequency noise, interference among nodes and from external sources, packet collision, etc. TOSSIM provides an accurate model of the mainstream MicaZ radio stack. , by taking into account bursts of interference) it is possible to add a statistical noise model over the original links’ signal strengths. This model is generated automatically from a trace measured experimentally in real-world testbeds, with the generation algorithm based on Closest Pattern Matching [8].

As we may observe, the MO-FA obtains higher values of HV in most of the data sets. In Figure 1, we present an illustrative comparison of the MOEAs. As we can see, the MO-FA is clearly better than the rest of approaches when the resources in the optical networks are limited (W =1, W =2, and W =3); however, it performs similar to the DEPT algorithm in the easiest scenarios (W =4). In order to ensure Routing Low-Speed Traffic onto High-Speed Lightpaths by Using MO-FA (a) W=1 (b) W=2 (c) W=3 (d) W=4 19 Fig.

X24,2 , . . , x1,Nday , x2,Nday , . . , x24,Nday , (4) subject to the following constraints: 1. the values of the controlled variables, namely the DERs power otuputs (both active and reactive), taking into account the required power reserve should lie in the range defined by an upper limit Pjgmax and a lower limit Pjgmin , (see Eq. 5 and 6), Pjgmin ≤ Pjg,h,d ≤ Pjgmin , g g,h,d Qj min ≤ Qj g ≤ Qj min , (5) (6) where Pjg,h,d and Qg,h,d represent respectively the active and reactive power j production at hour h of day d of the j-th DER; 2.

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