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      Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

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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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            Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information

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              Metaheuristics in combinatorial optimization

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                Author and article information

                Journal
                Environmental Modelling & Software
                Environmental Modelling & Software
                Elsevier BV
                13648152
                December 2014
                December 2014
                : 62
                :
                : 271-299
                Article
                10.1016/j.envsoft.2014.09.013
                b6f664bd-a36e-45e3-8aeb-485989f224c3
                © 2014
                History

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