Multi-dimensional least-squares polynomial curve fitting
Communications of the ACM
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Kriging as a surrogate fitness landscape in evolutionary optimization
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Evolutionary design and implementation of a hard disk drive servo control system
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
Computational Optimization and Applications
Extreme learning machine for predicting HLA-Peptide binding
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Automating the drug scheduling of cancer chemotherapy via evolutionary computation
Artificial Intelligence in Medicine
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolutionary Optimization with Dynamic Fidelity Computational Models
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
The Pareto-following variation operator as an alternative approximation model
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A systems approach to evolutionary multiobjective structural optimization and beyond
IEEE Computational Intelligence Magazine
Evolutionary Model Type Selection for Global Surrogate Modeling
The Journal of Machine Learning Research
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Automatic surrogate model type selection during the optimization of expensive black-box problems
Proceedings of the Winter Simulation Conference
Information Sciences: an International Journal
Evolution by adapting surrogates
Evolutionary Computation
An optimization algorithm employing multiple metamodels and optimizers
International Journal of Automation and Computing
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Surrogate-Assisted Memetic Algorithm (SAMA) is a hybrid evolutionary algorithm, particularly a memetic algorithm that employs surrogate models in the optimization search. Since most of the objective function evaluations in SAMA are approximated, the search performance of SAMA is likely to be affected by the characteristics of the models used. In this paper, we study the search performance of using different meta modeling techniques, ensembles, and multi-surrogates in SAMA. In particular, we consider the SAMA-TRF, a SAMA model management framework that incorporates a trust region scheme for interleaving use of exact objective function with computationally cheap local meta models during local searches. Four different metamodels, namely Gaussian Process (GP), Radial Basis Function (RBF), Polynomial Regression (PR), and Extreme Learning Machine (ELM) neural network are used in the study. Empirical results obtained show that while some metamodeling techniques perform best on particular benchmark problems, ensemble of metamodels and multisurrogates yield robust and improved solution quality on the benchmark problems in general, for the same computational budget.