The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Accelerating the Convergence of Evolutionary Algorithms by Fitness Landscape Approximation
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Comparison Of Methods For Using Reduced Models To Speed Up Design Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Using approximations to accelerate engineering design optimization
Using approximations to accelerate engineering design optimization
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
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Evolutionary Algorithms' (EAs') application to real world optimization problems often involves expensive fitness function evaluation. Naturally this has a crippling effect on the performance of any population based search technique such as EA. Estimating the fitness of individuals instead of actually evaluating them is a workable approach to deal with this situation. Optimization problems in real world often involve expensive fitness. In [14] and [15] we presented two EA models, namely DAFHEA (Dynamic Approximate Fitness based Hybrid Evolutionary Algorithm) and DAFHEA-II respectively. The original DAFHEA framework [14] reduces computation time by controlled use of meta-models generated by Support Vector Machine regression to partly replace actual fitness evaluation by estimation. DAFHEA-II [15] is an enhancement to the original framework in that it can be applied to problems that involve uncertainty. DAFHEA-II, incorporates a multiple-model based learning approach for the support vector machine approximator to filter out effects of noise [15]. In this paper we present further investigation on the performance of DAFHEA and DAFHEA-II.