On optimal static and dynamic parameter choices for fixed-target optimization-Reference-Cited by-同舟云学术

On optimal static and dynamic parameter choices for fixed-target optimization

Published:2022-07-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Vinokurov Dmitry¹,Buzdalov Maxim¹

Affiliation:

1. ITMO University, Saint Petersburg, Russia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3512290.3528875

Reference42 articles.

1. Aldeida Aleti and Irene Moser . 2016. A systematic literature review of adaptive parameter control methods for evolutionary algorithms. Comput. Surveys 49 ( 2016 ), 56:1--56:35. Aldeida Aleti and Irene Moser. 2016. A systematic literature review of adaptive parameter control methods for evolutionary algorithms. Comput. Surveys 49 (2016), 56:1--56:35.

2. Fast mutation in crossover-based algorithms

3. Denis Antipov Maxim Buzdalov and Benjamin Doerr. 2020. First Steps Towards a Runtime Analysis When Starting With a Good Solution. In Parallel Problem Solving from Nature - PPSN XVI. Number 12270 in Lecture Notes in Computer Science. 560--573. Denis Antipov Maxim Buzdalov and Benjamin Doerr. 2020. First Steps Towards a Runtime Analysis When Starting With a Good Solution. In Parallel Problem Solving from Nature - PPSN XVI. Number 12270 in Lecture Notes in Computer Science. 560--573.

4. Lazy parameter tuning and control

5. Denis Antipov and Benjamin Doerr. 2020. Runtime Analysis of a Heavy-Tailed (1 + (λ λ)) Genetic Algorithm on Jump Functions. In Parallel Problem Solving from Nature - PPSN XVI. Number 12270 in Lecture Notes in Computer Science. 545--559. Denis Antipov and Benjamin Doerr. 2020. Runtime Analysis of a Heavy-Tailed (1 + (λ λ)) Genetic Algorithm on Jump Functions. In Parallel Problem Solving from Nature - PPSN XVI. Number 12270 in Lecture Notes in Computer Science. 545--559.