By Anna I. Esparcia-Alcázar, Antonio M. Mora
This e-book constitutes the completely refereed post-conference complaints of the foreign convention at the purposes of Evolutionary Computation, EvoApplications 2014, held in Granada, Spain, in April 2014, colocated with the Evo* 2014 occasions EuroGP, EvoCOP, and EvoMUSART.
The seventy nine revised complete papers awarded have been conscientiously reviewed and chosen from 128 submissions. EvoApplications 2014 consisted of the next thirteen tracks: EvoCOMNET (nature-inspired ideas for telecommunication networks and different parallel and dispensed systems), EvoCOMPLEX (evolutionary algorithms and complicated systems), EvoENERGY (evolutionary computation in strength 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 development recognition), EvoINDUSTRY (nature-inspired thoughts in business settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for probability administration, safety and defence applications), EvoROBOT (evolutionary computation in robotics), EvoSTOC (evolutionary algorithms in stochastic and dynamic environments), and EvoBio (EC and similar innovations in bioinformatics and computational biology).
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Additional info for Applications of Evolutionary Computation: 17th European Conference, EvoApplications 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers
150–157. ACM (2000) A Hybrid Primal Heuristic for Robust Multiperiod Network Design Fabio D’Andreagiovanni1,2(B) , Jonatan Krolikowski2 , and Jonad Pulaj2 1 DFG Research Center MATHEON, Technical University Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany 2 Department of Optimization, Zuse-Institute Berlin (ZIB), Takustr. de Abstract. We investigate the Robust Multiperiod Network Design Problem, a generalization of the classical Capacitated Network Design Problem that additionally considers multiple design periods and provides solutions protected against traﬃc uncertainty.
In , Abraham and Grosan compare the genetic programming technique with other machine learning methods for intrusion detection  and show that genetic programming techniques outperform other techniques and are lightweight. The grammatical evolution technique is successfully employed for intrusion detection on wired networks  and on ad hoc networks . Sen and Clark  employ multi-objective evolutionary computation (MOEC) techniques in order to show how energy usage and detection ability can be traded oﬀ for resource-constrained networks.
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