Global optimization using hybrid approach
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
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A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
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ASAGA: an adaptive surrogate-assisted genetic algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Global optimization using hybrid approach
WSEAS Transactions on Mathematics
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AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Local models--an approach to distributed multi-objective optimization
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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Journal of Computational Physics
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ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
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Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es)
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Evolutionary Computation
Block-matching algorithm based on harmony search optimization for motion estimation
Applied Intelligence
An optimization algorithm employing multiple metamodels and optimizers
International Journal of Automation and Computing
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We present an overview of evolutionary algorithms that use empirical models of the fitness function to accelerate convergence, distinguishing between evolution control and the surrogate approach. We describe the Gaussian process model and propose using it as an inexpensive fitness function surrogate. Implementation issues such as efficient and numerically stable computation, exploration versus exploitation, local modeling, multiple objectives and constraints, and failed evaluations are addressed. Our resulting Gaussian process optimization procedure clearly outperforms other evolutionary strategies on standard test functions as well as on a real-world problem: the optimization of stationary gas turbine compressor profiles.