By Doina Bucur, Giovanni Iacca, Giovanni Squillero, Alberto Tonda (auth.), Anna I. Esparcia-Alcázar (eds.)
This booklet 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 provided have been conscientiously reviewed and chosen from 119 submissions. EvoApplications 2013 consisted of the next 12 tracks: EvoCOMNET (nature-inspired suggestions for telecommunication networks and different parallel and disbursed systems), EvoCOMPLEX (evolutionary algorithms and intricate systems), EvoENERGY (evolutionary computation in strength applications), EvoFIN (evolutionary and traditional 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 strategies in business settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for chance 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
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. Evolutionary Computation 8, 173–195 (2000) 11. : Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. it Abstract. This paper presents a novel nature-inspired multi-objective optimization algorithm.
Telecommunication Systems 28, 369–386 (2005) 5. : Multiobjective Metaheuristics for Traﬃc Grooming in Optical Networks. IEEE Transactions on Evolutionary Computation, 1–17 (2012) (available online since June 2012) 6. : Fireﬂy Algorithms for Multimodal Optimization. , Zeugmann, T. ) 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.
As we may observe, we start initializing the set of non-dominated solutions as empty. 2) and we generate N F random ﬁreﬂies. In the original FA (single-objective version), those ﬁreﬂies with better value of ﬁtness are considered more attractive to the rest of ﬁreﬂies ﬂying around. 1). In the case of a ﬁreﬂy xj attracts or dominates xi , the ﬁreﬂy xi moves towards xj according to equation 4. In equation 4, rij refers to the Euclidean distance between xi and xj . Furthermore, in this equation, the second term corresponds with the attraction, and the third one is devoted to add dispersion to the algorithm.
Applications of Evolutionary Computation: 16th European Conference, EvoApplications 2013, Vienna, Austria, April 3-5, 2013. Proceedings by Doina Bucur, Giovanni Iacca, Giovanni Squillero, Alberto Tonda (auth.), Anna I. Esparcia-Alcázar (eds.)