Download Computer and Information Sciences: 31st International by Tadeusz Czachórski, Erol Gelenbe, Krzysztof Grochla, Ricardo PDF

By Tadeusz Czachórski, Erol Gelenbe, Krzysztof Grochla, Ricardo Lent

This booklet constitutes the refereed lawsuits of the thirty first foreign Symposium on laptop and data Sciences, ISCIS 2016, held in Krakow, Poland, in October 2016.

The 29 revised complete papers offered have been rigorously reviewed and chosen from sixty five submissions. The papers are equipped in topical sections on shrewdpermanent algorithms; information type and processing; stochastic modelling; functionality overview; queuing structures; instant networks and defense; photograph processing and machine vision.

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Extra resources for Computer and Information Sciences: 31st International Symposium, ISCIS 2016, Kraków, Poland, October 27–28, 2016, Proceedings

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960–970. uk Abstract. Selection hyper-heuristics are high level search methodologies which control a set of low level heuristics while solving a given problem. Move acceptance is a crucial component of selection hyperheuristics, deciding whether to accept or reject a new solution at each step during the search process. This study investigates group decision making strategies as ensemble methods exploiting the strengths of multiple move acceptance methods for improved performance. The empirical results indicate the success of the proposed methods across six combinatorial optimisation problems from a benchmark as well as an examination timetabling problem.

PATAT 2000. LNCS, vol. 2079, p. 176. Springer, Heidelberg (2001) 6. : Metaheuristics in combinatorial optimization. Ann. Oper. Res. 140(1), 189–213 (2005) ¨ 7. : An investigation of tuning a memetic algorithm for cross-domain search. In: 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE (2016) 8. : Implementation of simple multiobjective memetic algorithms and its applications to knapsack problems. Int. J. Hybrid Intell. Syst. 1(1), 22–35 (2004) ¨ 9. : An iterated multi-stage selection hyper-heuristic.

LNCS, vol. 2079, p. 176. Springer, Heidelberg (2001) 7. : Channel assignment optimisation using a hyperheuristic. In: IEEE Conference on Cybernetic and Intelligent Systems, pp. 790–795, 1–3 December 2004 ¨ 8. : An iterated multi-stage selection hyper-heuristic. Eur. J. Oper. Res. 250(1), 77–90 (2016) 9. : Optimization by simulated annealing. Science 220, 671–680 (1983) ¨ 10. : A runtime analysis of simple hyper-heuristics: to mix or not to mix operators. In: Workshop on Foundations of Genetic Algorithms XII, pp.

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