Theoretical Study of Optimizing Rugged Landscapes with the cGA-Reference-Cited by-同舟云学术

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Theoretical Study of Optimizing Rugged Landscapes with the cGA

Published:2022 Issue: Volume: Page:586-599
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Friedrich Tobias,Kötzing Timo,Neumann Frank,Radhakrishnan Aishwarya

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-14721-0_41

Reference24 articles.

1. Aarts, E., Lenstra, J.K.: Local Search in Combinatorial Optimization. Wiley (1997)

2. Aishwaryaprajna, Rowe, J.E.: Noisy combinatorial optimisation by evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2019, pp. 139–140 (2019). https://doi.org/10.1145/3319619.3321955

3. Daolio, F., Liefooghe, A., Verel, S., Aguirre, H., Tanaka, K.: Problem features versus algorithm performance on rugged multiobjective combinatorial fitness landscapes. Evol. Comput. 25, 555–585 (2017)

4. Natural Computing Series;B Doerr,2020

5. Doerr, B.: The runtime of the compact genetic algorithm on jump functions. Algorithmica 83, 3059–3107 (2021). https://doi.org/10.1007/s00453-020-00780-w

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