Applications of Evolutionary Computation: 17th European by Anna I. Esparcia-Alcázar, Antonio M. Mora

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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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 [19], Abraham and Grosan compare the genetic programming technique with other machine learning methods for intrusion detection [19] 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 [20] and on ad hoc networks [21]. Sen and Clark [22] 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.

10% Malicious Collaborator Attackers Naive Hypocritical 1200 File-based Attacks 1000 800 600 400 200 0 0 10000 20000 30000 40000 50000 Cycles Fig. 3. File-based attacks over time in a network consisting of 10% collaborators 10% Malicious Collaborator Attackers Recommendation-based Attacks Naive Hypocritical 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 0 10000 20000 30000 40000 50000 Cycles Fig. 4. Recommendation-based attacks over time in a network consisting of 10% collaborators as recommendation-based attacks.

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