Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
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Population-based incremental learning with memory scheme for changing environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Memory-based immigrants for genetic algorithms in dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
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Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Case-based heuristic selection for timetabling problems
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Adaptive genetic algorithms applied to dynamic multiobjective problems
Applied Soft Computing
Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms
Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms
A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
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Expert Systems with Applications: An International Journal
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
ISCID '10 Proceedings of the 2010 International Symposium on Computational Intelligence and Design - Volume 02
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
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The supply trajectory of electric power for submerged arc magnesia furnace determines the yields and grade of magnesia grain during the manufacture process. As the two production targets (i.e., the yields and the grade of magnesia grain) are conflicting and the process is subject to changing conditions, the supply of electric power needs to be dynamically optimized to track the moving Pareto optimal set with time. A hybrid evolutionary multiobjective optimization strategy is proposed to address the dynamic multiobjective optimization problem. The hybrid strategy is based on two techniques. The first one uses case-based reasoning to immediately generate good solutions to adjust the power supply once the environment changes, and then apply a multiobjective evolutionary algorithm to accurately solve the problem. The second one is to learn the case solutions to guide and promote the search of the evolutionary algorithm, and the best solutions found by the evolutionary algorithm can be used to update the case library to improve the accuracy of case-based reasoning in the following process. Due to the effectiveness of mutual promotion, the hybrid strategy can continuously adapt and search in dynamic environments. Two prominent multiobjective evolutionary algorithms are integrated into the hybrid strategy to solve the dynamic multiobjective power supply optimization problem. The results from a series of experiments show that the proposed hybrid algorithms perform better than their component multiobjective evolutionary algorithms for the tested problems.