  Significant effort has been made to solve computationally expensive
optimization problems in the past two decades, and various optimization methods
incorporating surrogates into optimization have been proposed. Most research
focuses on either exploiting the surrogate by defining a utility optimization
problem or customizing an existing optimization method to use one or multiple
approximation models. However, only a little attention has been paid to generic
concepts applicable to different types of algorithms and optimization problems
simultaneously. Thus this paper proposes a generalized probabilistic
surrogate-assisted framework (GPSAF), applicable to a broad category of
unconstrained and constrained, single- and multi-objective optimization
algorithms. The idea is based on a surrogate assisting an existing optimization
method. The assistance is based on two distinct phases, one facilitating
exploration and another exploiting the surrogates. The exploration and
exploitation of surrogates are automatically balanced by performing a
probabilistic knockout tournament among different clusters of solutions. A
study of multiple well-known population-based optimization algorithms is
conducted with and without the proposed surrogate assistance on single- and
multi-objective optimization problems with a maximum solution evaluation budget
of 300 or less. The results indicate the effectiveness of applying GPSAF to an
optimization algorithm and the competitiveness with other surrogate-assisted
algorithms.
