Cited By
View all- Bartz-Beielstein T(2015)How to Create Generalizable ResultsSpringer Handbook of Computational Intelligence10.1007/978-3-662-43505-2_56(1127-1142)Online publication date: 2015
How to create meaningful and generalizable results
Pages 979 - 1004
References
[1]
Douglas M. Bates. lme4: Mixed-effects modeling with R. 2010.
[2]
G. E. P. Box, G. M. Jenkins, and G. C. Reinsel. Time Series Analysis, Forecasting and Control. Holden-Day, 1976.
[3]
Marco Chiarandini and Yuri Goegebeur. Mixed models for the analysis of optimization algorithms. In Thomas Bartz-Beielstein, Marco Chiarandini, Luís Paquete, and Mike Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pages 225--264. Springer, Germany, 2010. Preliminary version available as Tech. Rep. DMF-2009-07-001 at the The Danish Mathematical Society.
[4]
M. Gallagher and B. Yuan. A general-purpose tunable landscape generator. IEEE transactions on evolutionary computation, 10(5):590--603, 2006.
[5]
D. C. Montgomery. Design and Analysis of Experiments. Wiley, New York NY, 5th edition, 2001.
Index Terms
- How to create meaningful and generalizable results
Recommendations
Noisy optimization with sequential parameter optimization and optimal computational budget allocation
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationSequential parameter optimization (SPO) is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms. In this study, SPO is directly used as an optimization method on different noisy ...
Tuned data mining: a benchmark study on different tuners
GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computationThe complex, often redundant and noisy data in real-world data mining (DM) applications frequently lead to inferior results when out-of-the-box DM models are applied. A tuning of parameters is essential to achieve high-quality results. In this work we ...
On the effect of response transformations in sequential parameter optimization
Parameter tuning of evolutionary algorithms (EAs) is attracting more and more interest. In particular, the sequential parameter optimization (SPO) framework for the model-assisted tuning of stochastic optimizers has resulted in established parameter ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
July 2013
1798 pages
Copyright © 2013 Copyright is held by the owner/author(s).
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 06 July 2013
Check for updates
Author Tags
Qualifiers
- Tutorial
Conference
GECCO '13
Sponsor:
GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands
Acceptance Rates
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 106Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Nov 2024
Other Metrics
Citations
Cited By
View all- Bartz-Beielstein T(2015)How to Create Generalizable ResultsSpringer Handbook of Computational Intelligence10.1007/978-3-662-43505-2_56(1127-1142)Online publication date: 2015
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in