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GECCO
2004
Springer

Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles

11 years 10 months ago
Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles
Abstract. In many real-world applications of evolutionary computation, it is essential to reduce the number of fitness evaluations. To this end, computationally efficient models can be constructed for fitness evaluations to assist the evolutionary algorithms. When approximate models are involved in evolution, it is very important to determine which individuals should be re-evaluated using the original fitness function to guarantee a faster and correct convergence of the evolutionary algorithm. In this paper, the k-nearest-neighbor method is applied to group the individuals of a population into a number of clusters. For each cluster, only the individual that is closest to the cluster center will be evaluated using the expensive original fitness function. The fitness of other individuals are estimated using a neural network ensemble, which is also used to detect possible serious prediction errors. Simulation results from three test functions show that the proposed method exhibits be...
Yaochu Jin, Bernhard Sendhoff
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where GECCO
Authors Yaochu Jin, Bernhard Sendhoff
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