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

Gloria Pietropolli, Giuliamaria Menara, Mauro Castelli
2022 arXiv   pre-print
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
more » ... 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).
arXiv:2206.03241v1 fatcat:3tuj2pfjmrbzbo7hs7gvp6qq4e