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 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 awarded have been conscientiously reviewed and chosen from 119 submissions. EvoApplications 2013 consisted of the next 12 tracks: EvoCOMNET (nature-inspired recommendations for telecommunication networks and different parallel and allotted systems), EvoCOMPLEX (evolutionary algorithms and complicated systems), EvoENERGY (evolutionary computation in power 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 development 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, defense and defence applications), EvoROBOT (evolutionary computation in robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).
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Extra info for Applications of Evolutionary Computation: 16th European Conference, EvoApplications 2013, Vienna, Austria, April 3-5, 2013. Proceedings
A survey of automated techniques for formal software veriﬁcation. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 27(7), 1165–1178 (2008) 4. : A framework for automated detection of power-related software errors in industrial veriﬁcation processes. Journal of Electronic Testing 26(6), 689–697 (2010) 5. : Collection tree protocol. In: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, SenSys 2009, pp. 1–14. ACM, New York (2009) 6. : Wireless Sensor Network Anomalies: Diagnosis and Detection Strategies.
Step 3) Stopping Criterion: If the stopping criterion is satisﬁed, then stop and output P F . Otherwise, go to Step 2. 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.
Agents in GSO depend only on information available in their neighbourhood to make decisions. Each glow-worm selects, using a probabilistic mechanism, a neighbour that has a luciferin value higher than its own and moves toward it. These movements, that are based only on local information and selective neighbour interactions, enable the swarm of glow-worms to partition into disjoint subgroups that steer toward, and meet at, multiple optima of a given multimodal function. The GSO algorithm starts by placing a population of n glow-worms randomly in the search space so that they are well dispersed.