Tree-Based Grammar Genetic Programming to Evolve Particle Swarm Algorithms

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@InProceedings{conf/bracis/MirandaP16,
  author =       "Pericles B. C. Miranda and Ricardo B. C. Prudencio",
  title =        "Tree-Based Grammar Genetic Programming to Evolve
                 Particle Swarm Algorithms",
  booktitle =    "2016 5th Brazilian Conference on Intelligent Systems
                 (BRACIS)",
  year =         "2016",
  pages =        "25--30",
  address =      "Recife, Brazil",
  month =        "9-12",
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, genetic programming,
                 hyperheuristic, PSO",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/https://doi.org/10.1109/BRACIS.2016.016;
                 DBLP,
                 http://dblp.uni-trier.de/db/conf/bracis/bracis2016.html#MirandaP16",
  isbn13 =       "978-1-5090-3566-3",
  DOI =          "doi:10.1109/BRACIS.2016.016",
  size =         "6 pages",
  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.",
  notes =        "See also \cite{Miranda:2016:BRACIS} Also known as
                 \cite{7839557}",
}

Genetic Programming entries for Pericles Barbosa Miranda Ricardo Bastos Cavalcante Prudencio

Citations