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Abstract: Evolutionary algorithms are generally believed to perform well in the presence of noise. Thus, they are often used for optimization in noisy environments. It comes as a surprise that hardly more than a handful of studies has dealt with the question of just how well they are doing and what can be done to improve their performance. The present paper presents empirical results regarding the behavior of genetic algorithms and evolution strategies in the presence of fitness noise for a... (Update)
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...even in the presence of noise. In fact, many EA approaches have been applied to optimizing noisy objective functions effectively[1, 3, 4, 9, 15]. Back and Hammel[3, 9] observed the evolutionary process of ES on the sphere function and it s convergence reliability on Rastrigin s...
.... e#ect can be observed qualitatively in all EA 11 systems with fixed and # and constant fitness noise (including GA, see Beyer and Arnold [44]) When considering the results on # # one notices that progress toward the optimum is a result of two opposite tendencies: a positive...
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BibTeX entry: (Update)
H.-G. Beyer and D. Arnold (1999), "Fitness Noise and Localization Errors of the Optimum in General Quadratic Fitness Models", Proc. of Genetic and Evolutionary Computation Conference (GECCO'99), pp. 817-824, Morgan Kaufmann. http://citeseer.ist.psu.edu/beyer99fitness.html More
@inproceedings{ beyer99fitness,
author = "Hans-Georg Beyer and Dirk V. Arnold",
title = "Fitness Noise and Localization Errors of the Optimum in General Quadratic Fitness Models",
booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference",
volume = "1",
month = "13-17",
publisher = "Morgan Kaufmann",
address = "Orlando, Florida, USA",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith",
isbn = "1-55860-611-4",
pages = "817--824",
year = "1999",
url = "citeseer.ist.psu.edu/beyer99fitness.html" }
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