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Abstract: In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain... (Update)
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BibTeX entry: (Update)
E. Zitzler, K. Deb, and L. Thiele, "Comparison of multiobjective evolutionary algorithms: Empirical results," Tech. Rep. 70, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland, February 1999. http://citeseer.ist.psu.edu/zitzler00comparison.html More
@article{ zitzler99comparison,
Author = {Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele},
Journal = {Evolutionary Computation},
Number = {2},
Pages = {173--195},
Title = {Comparison of Multiobjective Evolutionary Algorithms: Empirical Results},
Url = {citeseer.ist.psu.edu/zitzler00comparison.html},
Url = {http://citeseer.ist.psu.edu/zitzler00comparison.html},
Volume = {8},
Year = {2000} }
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