Aerodynamic design via control theory
Journal of Scientific Computing
Generalized Hermite interpolation via matrix-valued conditionally positive definite functions
Mathematics of Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
A Computationally Efficient Feasible Sequential Quadratic Programming Algorithm
SIAM Journal on Optimization
A framework for managing models in nonlinear optimization of computationally expensive functions
A framework for managing models in nonlinear optimization of computationally expensive functions
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
A multi-cluster grid enabled evolution framework for aerodynamic airfoil design optimization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
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
Max-min surrogate-assisted evolutionary algorithm for robust design
IEEE Transactions on Evolutionary Computation
A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Structural optimization based on CAD-CAE integration and metamodeling techniques
Computer-Aided Design
Block-matching algorithm based on differential evolution for motion estimation
Engineering Applications of Artificial Intelligence
Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)
Applied Soft Computing
A novel evolutionary algorithm inspired by the states of matter for template matching
Expert Systems with Applications: An International Journal
Block-matching algorithm based on harmony search optimization for motion estimation
Applied Intelligence
Parameter-less algorithm for evolutionary-based optimization
Computational Optimization and Applications
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In this paper, we present an evolutionary algorithm hybridized with a gradient-based optimization technique in the spirit of Lamarckian learning for efficient design optimization. In order to expedite gradient search, we employ local surrogate models that approximate the outputs of a computationally expensive Euler solver. Our focus is on the case when an adjoint Euler solver is available for efficiently computing the sensitivities of the outputs with respect to the design variables. We propose the idea of using Hermite interpolation to construct gradient-enhanced radial basis function networks that incorporate sensitivity data provided by the adjoint Euler solver. Further, we conduct local search using a trust-region framework that interleaves gradient-enhanced surrogate models with the computationally expensive adjoint Euler solver. This ensures that the present hybrid evolutionary algorithm inherits the convergence properties of the classical trust-region approach. We present numerical results for airfoil aerodynamic design optimization problems to show that the proposed algorithm converges to good designs on a limited computational budget.