Journal of Global Optimization
Application of optimization techniques to parameter set-up in scheduling
Computers in Industry
Incorporating prior model into Gaussian processes regression for WEDM process modeling
Expert Systems with Applications: An International Journal
Simulation optimization based on Taylor Kriging and evolutionary algorithm
Applied Soft Computing
Expert Systems with Applications: An International Journal
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This paper is concerned with approximations for expensive function evaluation – the expensive functions arising in an engineering design context. The problem of reducing the computational cost of generating sufficient learning samples is addressed. Several approaches of using a priori knowledge to achieve computational economy are presented. In all these, the results of a cheap model are treated as knowledge to be incorporated in the training process. Several approaches are described here: in particular, we focus on neural based systems. This approach is then developed as a new knowledge-based kriging model which is shown to be as accurate as neural based alternatives while being much easier to train. Examples from the domain of structural optimization are given to demonstrate the approach.