Download Applications of Evolutionary Computation: 19th European by Giovanni Squillero, Paolo Burelli PDF

By Giovanni Squillero, Paolo Burelli

The volumes LNCS 9597 and 9598 represent the refereed convention lawsuits of the nineteenth eu convention at the purposes of Evolutionary Computation, EvoApplications 2016, held in Porto, Portugal, in March/April 2016, co-located with the Evo* 2016 occasions EuroGP, EvoCOP, and EvoMUSART.

The fifty seven revised complete papers provided including 17 poster papers have been rigorously reviewed and chosen from one hundred fifteen submissions. EvoApplications 2016 consisted of the subsequent thirteen tracks: EvoBAFIN (natural computing tools in company analytics and finance), EvoBIO (evolutionary computation, desktop studying and information mining in computational biology), EvoCOMNET (nature-inspired ideas for telecommunication networks and different parallel and disbursed systems), EvoCOMPLEX (evolutionary algorithms and intricate systems), EvoENERGY (evolutionary computation in strength applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in snapshot research, sign processing, and trend recognition), EvoINDUSTRY (nature-inspired recommendations in business settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for possibility administration, safety and defence applications), EvoROBOT (evolutionary robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).

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Additional resources for Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings, Part II

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Elsevier, Amsterdam (1992) 7. : Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992) 8. : A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Trans. Evol. Comput. 10(1), 19–28 (2006) 9. : Parameter control by the entire search history: case study of history-driven evolutionary algorithm. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8, July 2010 10.

14] propose to use a computationally cheap meta-model of the fitness function and tune the parameters of the evolutionary optimization approach on this surrogate. Kramer et al. [7] combine two nearest neighbor metamodels, one for the fitness function, and one for the constraint function with an adaptive penalty function in a constrained continuous optimization scenario. Most of the work sketched here positively reported savings in fitness function evaluations, although machine learning models and meta-model managing strategies vary significantly.

Comparison of meta-model size N = 20 and N = 500 on the Sphere function with (a) d = 2 and (b) d = 10. Our analysis of the neighborhood size k have shown that the choice k = 1 yields the best results in all cases. Larger choices slow down the optimization or let the optimization process stagnate, similar to the stagnation we observe for small archive sizes. Hence, we understand the nearest neighbor regression meta-models with k = 1 as local meta-models, which also belong to the most successful ones in literature, see Sect.

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