  Stochastic, iterative search methods such as Evolutionary Algorithms (EAs)
are proven to be efficient optimizers. However, they require evaluation of the
candidate solutions which may be prohibitively expensive in many real world
optimization problems. Use of approximate models or surrogates is being
explored as a way to reduce the number of such evaluations. In this paper we
investigated three such methods. The first method (DAFHEA) partially replaces
an expensive function evaluation by its approximate model. The approximation is
realized with support vector machine (SVM) regression models. The second method
(DAFHEA II) is an enhancement on DAFHEA to accommodate for uncertain
environments. The third one uses surrogate ranking with preference learning or
ordinal regression. The fitness of the candidates is estimated by modeling
their rank. The techniques' performances on some of the benchmark numerical
optimization problems have been reported. The comparative benefits and
shortcomings of both techniques have been identified.
