Machine Learning
ACM Computing Surveys (CSUR)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Memetic algorithm using multi-surrogates for computationally expensive optimization problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Multiobjective GA optimization using reduced models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
IEEE Transactions on Evolutionary Computation
EMORBFN: An Evolutionary Multiobjetive Optimization Algorithm for RBFN Design
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A memetic algorithm with non gradient-based local search assisted by a meta-model
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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In this paper, an evolutionary algorithm with spatially distributed surrogates (EASDS) for multiobjective optimization is presented. The algorithm performs actual analysis for the initial population and periodically every few generations. An external archive of the unique solutions evaluated using the actual analysis is maintained to train the surrogate models. The data points in the archive are split into multiple partitions using k-Means clustering. A Radial Basis Function (RBF) network surrogate model is built for each partition using a fraction of the points in that partition. The rest of the points in the partition are used as a validation data to decide the prediction accuracy of the surrogate model. Prediction of a new candidate solution is done by the surrogate model with the least prediction error in the neighborhood of that point. Five multiobjective test problems are presented in this study and a comparison with Nondominated Sorting Genetic Algorithm II (NSGA-II) is included to highlight the benefits offered by our approach. EASDS algorithm consistently reported better nondominated solutions for all the test cases for the same number of actual evaluations as compared to a single global surrogate model and NSGA-II.