Improved regression models for algorithm configuration-Reference-Cited by-同舟云学术

Improved regression models for algorithm configuration

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

de Souza Marcelo¹,Ritt Marcus²

Affiliation:

1. Universidade do Estado de Santa Catarina and Universidade Federal do Rio Grande do Sul

2. Universidade Federal do Rio Grande do Sul

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Google Research Latin America

CNPq

Universidade do Estado de Santa Catarina

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3512290.3528750

Reference44 articles.

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2. A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms

3. Thomas Bartz-Beielstein , C. Lasarczyk , and Mike Preuss . 2010. The Sequential Parameter Optimization Toolbox . In Experimental Methods for the Analysis of Optimization Algorithms, Thomas Bartz-Beielstein, Marco Chiarandini, Luís Paquete , and Mike Preuss (Eds.). Springer , Berlin, Germany , 337--360. Thomas Bartz-Beielstein, C. Lasarczyk, and Mike Preuss. 2010. The Sequential Parameter Optimization Toolbox. In Experimental Methods for the Analysis of Optimization Algorithms, Thomas Bartz-Beielstein, Marco Chiarandini, Luís Paquete, and Mike Preuss (Eds.). Springer, Berlin, Germany, 337--360.

4. John E. Beasley. 1998. Heuristic Algorithms for the Unconstrained Binary Quadratic Programming Problem. http://people.brunel.ac.uk/~mastjjb/jeb/bqp.pdf John E. Beasley. 1998. Heuristic Algorithms for the Unconstrained Binary Quadratic Programming Problem. http://people.brunel.ac.uk/~mastjjb/jeb/bqp.pdf

5. Feature Based Algorithm Configuration: A Case Study with Differential Evolution

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