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      Dynamic Multiobjective Optimization Problems: Test Cases, Approximations, and Applications

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      IEEE Transactions on Evolutionary Computation
      Institute of Electrical and Electronics Engineers (IEEE)

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          Comparison of multiobjective evolutionary algorithms: empirical results.

          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 technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.
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            Parameter control in evolutionary algorithms

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              Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems

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                Author and article information

                Journal
                IEEE Transactions on Evolutionary Computation
                IEEE Trans. Evol. Computat.
                Institute of Electrical and Electronics Engineers (IEEE)
                1089-778X
                October 2004
                October 2004
                : 8
                : 5
                : 425-442
                Article
                10.1109/TEVC.2004.831456
                8befdf58-f036-4f7a-9e00-2a62c7cbc761
                © 2004
                Product
                Self URI (article page): http://ieeexplore.ieee.org/document/1347158/

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