Genetic and Evolutionary Computing: Proceeding of the Eighth

This quantity of Advances in clever structures and Computing comprises accredited papers offered at ICGEC 2014, the eighth foreign convention on Genetic and Evolutionary Computing. The convention this yr was once technically co-sponsored by means of Nanchang Institute of know-how in China, Kaohsiung college of utilized technological know-how in Taiwan, and VSB-Technical collage of Ostrava. ICGEC 2014 is held from 18-20 October 2014 in Nanchang, China. Nanchang is one among is the capital of Jiangxi Province in southeastern China, situated within the north-central part of the province. because it is bounded at the west through the Jiuling Mountains, and at the east through Poyang Lake, it's recognized for its surroundings, wealthy heritage and cultural websites. as a result of its valuable position relative to the Yangtze and Pearl River Delta areas, it's a significant railroad hub in Southern China. The convention is meant as a world discussion board for the researchers and execs in all components of genetic and evolutionary computing

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Additional info for Genetic and Evolutionary Computing: Proceeding of the Eighth International Conference on Genetic and Evolutionary Computing, October 18–20, 2014, Nanchang, China (Advances in Intelligent Systems and Computing, Volume 329)

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Scenario approximations of chance constraints. , Dabbene, F. ) Probabilistic and Randomized Methods for Design under Uncertainty, pp. 3–48. Springer, London (2005) 4. : Noisy immune optimization for chance-constrained programming problems. Applied Mechanics and Materials 48, 740–744 (2011) 5. : Genetic algorithm based technique for solving chance constrained problems. European Journal of Operational Research 185(3), 1128–1154 (2008) 6. : Recent advances in artificial immune systems: models and applications.

Especially, the three approaches of HPSO, SSGA-A and SSGA-B are static sampling optimization approaches with the same fixed sample size for each individual, whereas NIOA is an adaptive sampling optimization approach with a dynamic sample size for each individual. Their parameter settings are the same as those in their corresponding literature except their evolving population sizes. All the above algorithms take their population sizes 40, while respectively executing 30 times on each test problem.

Applied Soft Computing 11(2), 1574–1587 (2011) 7. : An immune clonal hybrid algorithm for solving stochastic chance-constrained programming. Journal of Computational Information Systems 8(20), 8295–8302 (2012) 8. : Adaptive sampling immune algorithm solving joint chance-constrained programming. Journal of Control Theory and Application 11(2), 237–246 (2013) 9. : Efficient sampling for simulation-based optimization under uncertainty. In: Proceedings of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA 2003), pp.

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