The irace package: Iterated racing for automatic algorithm configuration
Manuel López-Ibáñez,Jérémie Dubois-Lacoste,Leslie Pérez Cáceres,Mauro Birattari,Thomas Stützle +4 more
TLDR
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.About:
This article is published in Operations Research Perspectives.The article was published on 2016-01-01 and is currently open access. It has received 1280 citations till now.read more
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Recent advances in differential evolution – An updated survey
TL;DR: It is found that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research on DE.
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Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
TL;DR: In this article, the problem of simultaneously selecting a learning algorithm and setting its hyperparameters is addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization, which can help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications.
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Ant Colony Optimization: Overview and Recent Advances
Marco Dorigo,Thomas Stützle +1 more
TL;DR: This chapter reviews developments in ACO and gives an overview of recent research trends, including the development of high-performing algorithmic variants and theoretical understanding of properties of ACO algorithms.
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Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA
TL;DR: The new version of Auto-WEKA is described, a system designed to help novice users by automatically searching through the joint space of WEKA's learning algorithms and their respective hyperparameter settings to maximize performance, using a state-of-the-art Bayesian optimization method.
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Iterated Local Search: Framework and Applications
TL;DR: The purpose here is to give an accessible description of the underlying principles of iterated local search and a discussion of the main aspects that need to be taken into account for a successful application of it.
References
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