Bi-objective parameter setting problem of a genetic algorithm: an empirical study on traveling salesperson problem
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-04972-z.pdf
Reference38 articles.
1. Coy SP, Golden BL, Runger GC, Wasil EA (2001) Using experimental design to find effective parameter settings for heuristics. J Heuristics 7(1):77–97
2. De Jong K (2007) Parameter setting in EAs: a 30 year perspective. In: Lobo FG, Lima CF, Michalewicz Z (eds) Parameter Setting in Evolutionary Algorithms. Springer, Berlin, Heidelberg, p 1–18
3. Huang CW, Li YX, Yao X (2020) A survey of automatic parameter tuning methods for metaheuristics. IEEE Trans Evol Comput 24(2):201–216
4. Ha QM, Deville Y, Pham QD, Ha MH (2020) A hybrid genetic algorithm for the traveling salesman problem with drone. J Heuristics 26(2):219–247
5. Zhang PL, Wang JQ, Tian ZW, Sun SZ, Li JT, Yang JN (2022) A genetic algorithm with jumping gene and heuristic operators for traveling salesman problem. Appl Soft Comput 127:109339
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