Experimental Analysis of the Tournament Size on Genetic Algorithms

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

@InProceedings{Lavinas:2018:ieeeSMC,

• author = "Yuri Lavinas and Claus Aranha and Tetsuya Sakurai and Marcelo Ladeira",
• booktitle = "2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",
• title = "Experimental Analysis of the Tournament Size on Genetic Algorithms",
• year = "2018",
• pages = "3647--3653",
• abstract = "We perform an experimental study about the effect of the tournament size parameter from the Tournament Selection operator. Tournament Selection is a classic operator for Genetic Algorithms and Genetic Programming. It is simple to implement and has only one control parameter, the tournament size. Even though it is commonly used, most practitioners still rely on rules of thumb when choosing the tournament size. For example, almost all works in the past 15 years use a value of 2 for the tournament size, with little reasoning behind that choice. To understand the role of the tournament size, we run a real-valued GA on 24 BBOB problems with 10, 20 and 40 dimensions. We also vary the crossover operator and the generational policy of the GA. For each combination of the above factors we observe how the quality of the final solution changes with the tournament size. Our findings do not support the indiscriminate use of tournament size 2, and recommend a more careful set up of this parameter.",
• keywords = "genetic algorithms, genetic programming, Sociology, Statistics, Convergence, Benchmark testing, Decision making, Next generation networking",
• DOI = "doi:10.1109/SMC.2018.00617",
• ISSN = "2577-1655",
• month = oct,
• notes = "Also known as \cite{8616614}",

}

Genetic Programming entries for Yuri Lavinas Claus de Castro Aranha Tetsuya Sakurai Marcelo Ladeira

Citations