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MAX-MIN Ant System
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Sequential Model-Based Optimization for General Algorithm Configuration
TLDR
This paper extends the explicit regression models paradigm for the first time to general algorithm configuration problems, allowing many categorical parameters and optimization for sets of instances, and yields state-of-the-art performance.
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Stochastic Local Search: Foundations & Applications
TLDR
This prologue explains the background to SLS, and some examples of applications can be found in SAT and Constraint Satisfaction, as well as some of the algorithms used to solve these problems.
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
TLDR
This work considers the problem of simultaneously selecting a learning algorithm and setting its hyperparameters, going beyond previous work that attacks these issues separately and shows classification performance often much better than using standard selection and hyperparameter optimization methods.
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ParamILS: An Automatic Algorithm Configuration Framework
TLDR
An automatic framework for this algorithm configuration problem is described and methods for optimizing a target algorithm's performance on a given class of problem instances by varying a set of ordinal and/or categorical parameters are provided.
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CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements
TLDR
This paper proposes a qualitative graphical representation of preferences that reflects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation, and provides a formal semantics for this model.
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CP-nets: a tool for represent-ing and reasoning with conditional ceteris paribus state-ments
MAX-MIN Ant System and local search for the traveling salesman problem
  • T. Stutzle, H. Hoos
  • Computer Science
    Proceedings of IEEE International Conference on…
  • 13 April 1997
TLDR
The results clearly show that MAX-MIN Ant System has the property of effectively guiding the local search heuristics towards promising regions of the search space by generating good initial tours.
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SATzilla: Portfolio-based Algorithm Selection for SAT
TLDR
SATzilla is described, an automated approach for constructing per-instance algorithm portfolios for SAT that use so-called empirical hardness models to choose among their constituent solvers and is improved by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances.
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Critical assessment of automated flow cytometry data analysis techniques
TLDR
Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.
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