Andy J. Keane

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Engineering design problems requiring the construction of a cheap-to-evaluate ‘surrogate’ model f̂ 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 f x 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)
This paper briefly reviews the behaviour of four different evolutionary optimization methods when applied to a pair of difficult, high dimension test functions. The methods considered are the Genetic Algorithm (GA)1, Evolutionary Programming (EP)2, Evolution Strategies (ES)3 and Simulated Annealing (SA)4. The two problems considered are the royal road(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)
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)
The control of structural vibration in cars, aeroplanes, ships, etc., is of great importance in achieving low noise targets. Currently, such control is effected using viscoelastic coating materials although much current research is concerned with active, anti-noise based control measures. This paper is concerned with a third approach: that of using advanced(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)
It is often the case in many problems in science and engineering that the analysis codes used are computationally very expensive. This can pose a serious impediment to the successful application of evolutionary optimization techniques. Metamodeling techniques present an enabling methodology for reducing the computational cost of such optimization problems.(More)
Striking the correct balance between global exploration of search spaces and local exploitation of promising basins of attraction is one of the principal concerns in the design of global optimization algorithms. This is true in the case of techniques based on global response surface approximation models as well. After constructing such a model using some(More)