  Most real-world optimization problems are difficult to solve with traditional
statistical techniques or with metaheuristics. The main difficulty is related
to the existence of a considerable number of local optima, which may result in
the premature convergence of the optimization process. To address this problem,
we propose a novel heuristic method for constructing a smooth surrogate model
of the original function. The surrogate function is easier to optimize but
maintains a fundamental property of the original rugged fitness landscape: the
location of the global optimum. To create such a surrogate model, we consider a
linear genetic programming approach enhanced by a self-tuning fitness function.
The proposed algorithm, called the GP-FST-PSO Surrogate Model, achieves
satisfactory results in both the search for the global optimum and the
production of a visual approximation of the original benchmark function (in the
2-dimensional case).
