What is it about?
In this paper, 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.
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Why is it important?
Small scale parameter setting is effective for the large scale runs of the GA. 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.
Perspectives
This paper opens a new view to find the best parameter setting and parameter initiation value for the evolutionary algorithm. It could be continued by other researchers to create a package with which a researcher may add it to his/her GA algorithm and automatically find the best setting before running the algorithm.
Dr. Mohsen Mosayebi
University of Rhode Island
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This page is a summary of: Tuning genetic algorithm parameters using design of experiments, July 2020, ACM (Association for Computing Machinery),
DOI: 10.1145/3377929.3398136.
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