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

• URL: http://arxiv.org/abs/2206.03241v1
• Date: Tue, 7 Jun 2022 12:55:04 GMT
• Title: Combining Genetic Programming and Particle Swarm Optimization to Simplify Rugged Landscapes Exploration
• Authors: Gloria Pietropolli, Giuliamaria Menara, Mauro Castelli
• Abstract summary: We propose a novel method for constructing a smooth surrogate model of the original 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.
• Score: 7.25130576615102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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