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Engineering design problems requiring the construction of a cheap-to-evaluate 'surrogate' model f that emulates the expensive response of some black box f come in a variety of forms, but they can generally be distilled down to the following template. Here ffx is some continuous quality, cost or performance metric of a product or process defined by a(More)
Over the last decade, memetic algorithms have relied on the use of a variety of different methods as the local improvement procedure. Some recent studies on the choice of local search method employed have shown that this choice significantly affects the efficiency of problem searches. Given the restricted theoretical knowledge available in this area and the(More)
In this paper, we present a novel surrogate-assisted evolutionary optimization framework for solving computationally expensive problems. The proposed framework uses computationally cheap hierarchical surrogate models constructed through online learning to replace the exact computationally expensive objective functions during evolutionary search. At the(More)
We present some greedy learning algorithms for building sparse nonlinear regression and classification models from observational data using Mercer kernels. Our objective is to develop efficient numerical schemes for reducing the training and runtime complexities of kernel-based algorithms applied to large datasets. In the spirit of Natarajan's greedy(More)
In many areas of design search and optimisation one needs to utilize Computational Fluid Dynamics (CFD) methods in order to obtain a numerical solution of the flow field in and/or around a proposed design. From this solution measures of quality for the design may be calculated, which are then used by the optimisation methods. In large models the processing(More)
Efficient methods for global aerodynamic optimization using computational fluid dynamics simulations should aim to reduce both the time taken to evaluate design concepts and the number of evaluations needed for optimization. This paper investigates methods for improving such efficiency through the use of partially converged computational fluid dynamics(More)
This paper demonstrates the application of correlated Gaussian process based approximations to optimization where multiple levels of analysis are available, using an extension to the geostatistical method of co-kriging. An exchange algorithm is used to choose which points of the search space to sample within each level of analysis. The derivation of the(More)
In this paper we discuss the use of Grid services, an emerging Internet-based technology, to enable the application of numerical optimisation algorithms in heterogeneous, distributed systems for engineering design optimisation tasks. By being presented as Grid services, numerical optimisation algorithms can be consumed with a number of message interactions.(More)
The optimization of noisy or imprecisely specified functions is a common problem occurring in various applications. Models of physical systems can differ according to computational cost, accuracy and precision. In multilevel optimization, where different models of a system are used, there is a great benefit in understanding how many, fast evaluations of(More)