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Abstract: Multi-Objective Evolutionary Algorithms (MOEAs) have been the subject of numer- ous studies over the past 20 years. Recent work has highlighted the use of an active archive of elite, non-dominated solutions to improve the optimisation speed of these algorithms. (Update)
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BibTeX entry: (Update)
J.E. Fieldsend, R.M. Everson, and S.Singh. Using Unconstrained Elite Archives for Multi-Objective Optimisation. IEEE Transactions on Evolutionary Computation (Submitted), 2001. http://citeseer.ist.psu.edu/fieldsend01using.html More
@misc{ fieldsend01using,
author = "J. Fieldsend and R. Everson and S. Singh",
title = "Using Unconstrained Elite Archives for Multi-Objective Optimisation",
text = "J.E. Fieldsend, R.M. Everson, and S.Singh. Using Unconstrained Elite Archives
for Multi-Objective Optimisation. IEEE Transactions on Evolutionary Computation
(Submitted), 2001.",
year = "2001",
url = "citeseer.ist.psu.edu/fieldsend01using.html" }
Citations (may not include all citations):191 An Overview of Evolutionary Algorithms in Multiobjective Opt.. - Fonseca, Fleming - 1995 DBLP
162 Genetic Algorithms for Multiobjective Optimization: Formulat.. - Fonseca, Fleming - 1993
127 A Niched Pareto Genetic Algorithm for Multiobjective Optimiz.. - Horn, Nafpliotis et al. - 1994 DBLP
99 Comparison of Multiobjective Evolutionary Algorithms: Empiri.. - Zitzler, Deb et al. - 2000 DBLP
91 A Comprehensive Survey of Evolutionary-Based Multiobjective .. - Coello - 1999
54 Multiobjective Evolutionary Algorithms: Analyzing the State-.. - Van Veldhuizen, Lainont - 2000 DBLP
The graph only includes citing articles where the year of publication is known.
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