- author = "Mohsen Mosayebi and Manbir Sodhi",
- title = "Tuning Genetic Algorithm Parameters using Design of Experiments",
- booktitle = "9th edition of GI @ GECCO 2020",
- year = "2020",
- month = jul # " 8-12",
- editor = "Brad Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward",
- publisher = "ACM",
- address = "Internet",
- pages = "1937--1944",
- organisation = "SIGEVO",
- keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, TSP, Tuning Parameters, Design of Experiments",
- isbn13 = "978-1-4503-7127-8",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2020/companion_files/wksp176s2-file1.pdf",
-
DOI = "
doi:10.1145/3377929.3398136",
- size = "8 pages",
- abstract = "Tuning evolutionary algorithms is a persistent challenge in thefield of evolutionary computing. The efficiency of an evolutionary algorithm relates to the coding of the algorithm, the design of the evolutionary operators and the parameter settings for evolution. we explore the effect of tuning the operators and parameters of a genetic algorithm for solving the Traveling Salesman Problem using Design of Experiments theory. Small scale problems are solved with specific settings of parameters including population size, crossover rate, mutation rate and the extent of elitism. Good values of the parameters suggested by the experiments are used to solve large scale problems. Computational tests show that the parameters selected by this process result in improved performance both in the quality of results obtained and the convergence rate when compared with untuned parameter settings.",
-
notes = "TSPLIB gr17, eli101, pr1002
See also PhD {"}the traveling salesman problem with job-times, multi-circuit, and variations{"} Mohsen Mosayebi, University of Rhode Island https://doi.org/10.23860/Mosayebi-Mohsen-2020
Broken Nov 2024 https://gi-gecco-20.gi-workshops.org/
Also known as \cite{Mosayebi:2020:GECCOcomp}. Also known as \cite{10.1145/3377929.3398136} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",
