Open AccessProceedings Article
Model-based genetic algorithms for algorithm configuration
Carlos Ansótegui,Yuri Malitsky,Horst Samulowitz,Meinolf Sellmann,Kevin Tierney +4 more
- pp 733-739
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TLDR
A new model designed specifically for the task of predicting high-performance regions in the parameter space is introduced, and the ideas of genetic engineering of offspring as well as sexual selection of parents are introduced.Abstract:
Automatic algorithm configurators are important practical tools for improving program performance measures, such as solution time or prediction accuracy. Local search approaches in particular have proven very effective for tuning algorithms. In sequential local search, the use of predictive models has proven beneficial for obtaining good tuning results. We study the use of non-parametric models in the context of population-based algorithm configurators. We introduce a new model designed specifically for the task of predicting high-performance regions in the parameter space. Moreover, we introduce the ideas of genetic engineering of offspring as well as sexual selection of parents. Numerical results show that model-based genetic algorithms significantly improve our ability to effectively configure algorithms automatically.read more
Citations
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Journal ArticleDOI
The irace package: Iterated racing for automatic algorithm configuration
Manuel López-Ibáñez,Jérémie Dubois-Lacoste,Leslie Pérez Cáceres,Mauro Birattari,Thomas Stützle +4 more
TL;DR: The rationale underlying the iterated racing procedures in irace is described and a number of recent extensions are introduced, including a restart mechanism to avoid premature convergence, the use of truncated sampling distributions to handle correctly parameter bounds, and an elitist racing procedure for ensuring that the best configurations returned are also those evaluated in the highest number of training instances.
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Automated Algorithm Selection: Survey and Perspectives
TL;DR: This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection.
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Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices
Benjamin Doerr,Carola Doerr +1 more
TL;DR: This chapter surveys running-time results for a broad range of different parameter control mechanisms, and puts them into context by proposing an updated classification scheme for parameter control.
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Deep learning assisted heuristic tree search for the container pre-marshalling problem
TL;DR: This work integrates deep neural networks into a heuristic tree search procedure to decide which branch to choose next and to estimate a bound for pruning the search tree of an optimization problem.
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Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein,Carola Doerr,Jakob Bossek,Sowmya Chandrasekaran,Tome Eftimov,Andreas Fischbach,Pascal Kerschke,Manuel López-Ibáñez,Katherine M. Malan,Jason H. Moore,Boris Naujoks,Patryk Orzechowski,Vanessa Volz,Markus Wagner,Thomas Weise +14 more
TL;DR: The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility.
References
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