A Q-Learning Hyperheuristic Binarization Framework to Balance Exploration and Exploitation-Reference-Cited by-同舟云学术

A Q-Learning Hyperheuristic Binarization Framework to Balance Exploration and Exploitation

Published:2020 Issue: Volume: Page:14-28
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
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Author:

Tapia Diego^ORCID,Crawford Broderick^ORCID,Soto Ricardo^ORCID,Cisternas-Caneo Felipe^ORCID,Lemus-Romani José^ORCID,Castillo Mauricio^ORCID,García José^ORCID,Palma Wenceslao^ORCID,Paredes Fernando^ORCID,Misra Sanjay^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-61702-8_2

Reference35 articles.

1. Beasley, J.E.: Or-library: distributing test problems by electronic mail. J. Oper. Res. Soc. 41(11), 1069–1072 (1990). http://www.jstor.org/stable/2582903

2. Bishop, C.M.: Pattern Recoginiton and Machine Learning (2006)

3. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003). https://doi.org/10.1145/937503.937505

4. Book, R.V.: Book review: computers and intractability: a guide to the theory of NP-completeness. Bull. Am. Math. Soc. 3(2), 898–905 (1980). https://doi.org/10.1090/s0273-0979-1980-14848-x

5. Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. In: Glover, F., Kochenberger, G.A. (eds) Handbook of Metaheuristics. Springer, Boston(2006). https://doi.org/10.1007/0-306-48056-5_16

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