Tree-based Grammar Genetic Programming to Evolve Particle Swarm Algorithms

Abstract

Particle Swarm Optimization (PSO) is largely used to solve optimization problems effectively. Nonetheless, the PSO performance depends on the fine tuning of different parameters. To make the algorithm design process more independent from human intervention, some researchers have treated this task as an optimization problem. Grammar-guided Genetic Programming algorithms (GGGP), in special, have been widely studied and applied in the context of algorithm optimization. GGGP algorithms produce customized designs based on a set of production rules defined in the grammar; differently from methods that simply select designs in a pre-defined limited search space. In this work, we proposed a tree-based GGGP technique for the generation of PSO algorithms. This paper intends to investigate whether this approach can improve the production of PSO algorithms when compared to other GGGP techniques already used to solve the current problem. In the experiments, a comparison between the tree-based and the commonly used linearized GGGP approach for the generation of PSO algorithms was performed. The results showed that the tree-based GGGP produced better algorithms than the counterparts. We also compared the algorithms generated by the tree-based technique to state-of-art optimization algorithms, and the results showed that the algorithms produced by the tree-based GGGP achieved competitive results.