Comparison of Parallel Linear Genetic Programming Implementations

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Grochol2017,

• author = "David Grochol and Lukas Sekanina",
• title = "Comparison of Parallel Linear Genetic Programming Implementations",
• booktitle = "Proceedings of the 22nd International Conference on Soft Computing (MENDEL 2016)",
• year = "2016",
• editor = "Radek Matousek",
• volume = "576",
• series = "AISC",
• pages = "64--76",
• address = "Brno, Czech Republic",
• month = jun # " 8-10",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, parallel GP",
• isbn13 = "978-3-319-58087-6",
• ISSN = "2194-5357",
• DOI = "doi:10.1007/978-3-319-58088-3_7",
• abstract = "Linear genetic programming (LGP) represents candidate programs as sequences of instructions for a register machine. In order to accelerate the evaluation time of candidate programs and reduce the overall time of evolution, we propose various parallel implementations of LGP suitable for the current multi-core processors. The implementations are based on a parallel evaluation of candidate programs and the island model of the parallel evolutionary algorithm in which the subpopulations are evolved independently, but some genetic material can be exchanged by means of the migration. Proposed implementations are evaluated using three symbolic regression problems and a hash function design problem.",
• notes = "https://link.springer.com/book/10.1007/978-3-319-58088-3 ICSC-MENDEL 2016 Recent Advances in Soft Computing",

}

Genetic Programming entries for David Grochol Lukas Sekanina

Citations