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

7 Jun 2022  ·  Gloria Pietropolli, Giuliamaria Menara, Mauro Castelli ·

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).

PDF Abstract
No code implementations yet. Submit your code now



  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here