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Download e-book for kindle: Applications of Evolutionary Computation: 19th European by Giovanni Squillero, Paolo Burelli

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 awarded 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 equipment in company analytics and finance), EvoBIO (evolutionary computation, computer studying and knowledge mining in computational biology), EvoCOMNET (nature-inspired strategies for telecommunication networks and different parallel and dispensed systems), EvoCOMPLEX (evolutionary algorithms and complicated systems), EvoENERGY (evolutionary computation in strength applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in photo research, sign processing, and trend recognition), EvoINDUSTRY (nature-inspired suggestions in commercial settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for probability administration, defense 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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13] evaluated the diversity mechanisms of opt-ainet compared to fitness-sharing methods using a set of continuous optimization benchmarks, finding that the diversity mechanisms within opt-ainet both obtained better solutions but also maintained more diverse solutions. Most infusion techniques described in the literature rely on random initialisation methods; in contrast, we propose an operator that biases the generation of new individuals towards areas of the search space that are under-explored by the current population.

4. 6 Conclusions Meta-modeling in evolutionary optimization is frequent and well-known technique that allows saving expensive fitness function evaluations. In this paper, we Local Fitness Meta-Models with Nearest Neighbor Regression 9 applied nearest neighbor regression in a (1+1)-ES with Gaussian mutation and Rechenberg’s adaptive step size control. Fitness function evaluations are saved, if the predicted fitness is worse than the a-th best element in the archive. In particular on the Sphere function, the optimization runs of the (1+1)-ES with meta-model outperform the native ES.

Sawtooth (30 Dimensions) 21 22 A. Salah et al. shows the results for the 30-dimensional tests where we see that all instances of GDO configured opt-ainet algorithm were able to achieve better results than GDO-free opt-ainet, while all GDO configurations have utilised more function evaluations than GDO-free. The sawtooth algorithm for two-dimensional problems results (Fig. 5) shows that only the GDO-5 configuration failed to achieve better quality solutions than GDO-free sawtooth. In this case, GDO-2, GDO-7 and GDO-9 configurations shown faster convergence while the rest were the worst with respect to number of evaluations used.

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