Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
The value of online adaptive search: a performance comparison of NSGAII, ε-NSGAII and εMOEA
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Environmental Modelling & Software
Environmental Modelling & Software
Multi-objective calibration and fuzzy preference selection of a distributed hydrological model
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
International Journal of Advanced Intelligence Paradigms
Scour depth modelling by a multi-objective evolutionary paradigm
Environmental Modelling & Software
Many-objective de Novo water supply portfolio planning under deep uncertainty
Environmental Modelling & Software
Many objective robust decision making for complex environmental systems undergoing change
Environmental Modelling & Software
Environmental Modelling & Software
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Monitoring complex environmental systems is extremely challenging because it requires environmental professionals to capture impacted systems' governing processes, elucidate human and ecologic risks, limit monitoring costs, and satisfy the interests of multiple stakeholders (e.g., site owners, regulators, and public advocates). Evolutionary multiobjective optimization (EMO) has tremendous potential to help resolve these issues by providing environmental stakeholders with a direct understanding of their monitoring tradeoffs. This paper demonstrates how @?-dominance archiving and automatic parameterization techniques can be used to significantly improve the ease-of-use and efficiency of EMO algorithms. Results are presented for a four-objective groundwater monitoring design problem in which the archiving and parameterization techniques are combined to reduce computational demands by more than 90% relative to prior published results. The methods of this paper can be easily generalized to other multiobjective applications to minimize computational times as well as trial-and-error parameter analysis.