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Most real-world optimization problems are difficult to solve with traditionalstatistical techniques or with metaheuristics. The main difficulty is relatedto the existence of a considerable number of local optima, which may result inthe premature convergence of the optimization process. To address this problem,we propose a novel heuristic method for constructing a smooth surrogate modelof the original function. The surrogate function is easier to optimize butmaintains a fundamental property of the original rugged fitness landscape: thelocation of the global optimum. To create such a surrogate model, we consider alinear genetic programming approach enhanced by a self-tuning fitness function.The proposed algorithm, called the GP-FST-PSO Surrogate Model, achievessatisfactory results in both the search for the global optimum and theproduction of a visual approximation of the original benchmark function (in the2-dimensional case).

Combining Genetic Programming and Particle Swarm Optimization to Simplify Rugged Landscapes Exploration