Automatic Algorithm Configuration Based on Local Search

By Frank Hutter, Holger H. Hoos, Thomas Stützle. AAAI Conference on Artificial Intelligence, 2007

342 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e4776b8295629c8e74eb7503b3d6364caa026bb6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Automated Configuration of Mixed Integer Programming Solvers (2010, methodology): “Algorithm configuration procedures such as PARAMILS can in principle deal with various optimization objectives; in our own previous work, for example, we have optimized median runlength, average speedup over an existing algorithm, and average solution quality [20, 15].”
A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms (2009, methodology): “We proposed a genetic algorithm for this task and showed that it robustly provides high quality parameter sets which can significantly improve those of the pioneering system from [9].”
Algorithm Selection for Combinatorial Search Problems: A Survey (2012, methodology): “Hydra uses ParamILS (Hutter, Hoos, & St¨utzle, 2007; Hutter, Hoos, Leyton-Brown, & St¨utzle, 2009) to automatically tune algorithms in a SATzilla (Xu et al., 2008) portfolio.”
ParamILS: An Automatic Algorithm Configuration Framework (2014, methodology): “CALIBRA starts by evaluating each parameter conﬁguration in a full factorial design with two values per parameter.”
A Survey of Automatic Parameter Tuning Methods for Metaheuristics (2020, background): “[28], [81], is an automatic parameter tuning framework.”
Sequential Model-Based Optimization for General Algorithm Configuration (2011, methodology): “The most prominent configuration methods are the racing algorithm F-RACE [5] and our own iterated local search algorithm PARAMILS [7, 8].”
A bilevel optimization approach to automated parameter tuning (2014, methodology): “Some new extensions of the techniques in meta-optimization have been addressed in FocusedILS [21] and ParamILS [20].”
Automated Algorithm Configuration and Parameter Tuning (2012, methodology): “…to approaches based on experimental design methods (see, e.g., Birattari et al, 2002; Balaprakash et al, 2007; Adenso-Diaz and Laguna, 2006), response-surface models (see, e.g., Jones et al, 1998; Bartz-Beielstein, 2006) or stochastic local search procedures (see, e.g., Hutter et al, 2007b, 2009a).”
Hydra-MIP : Automated Algorithm Configuration and Selection for Mixed Integer Programming (2011, background): “A variety of problem-independent algorithm configuration procedures have also been proposed in the AI community, including I/F-Race [3], ParamILS [15, 14], and GGA [2].”
Automated configuration of algorithms for solving hard computational problems (2009, methodology): “In particular, we automatically constructed different instantiations of the SPEAR algorithm and thereby substantially improved the state of the art for two sets of SAT-encoded industrial verification problems (Hutter et al., 2007a).”
Boosting Verification by Automatic Tuning of Decision Procedures (2007, methodology): “[7], was used during the development of S PEAR, a high-performance modular arithmetic decision procedure and SAT solver, whic h was developed in support of the C ALYSTO static checker [8].”
The irace package: Iterated racing for automatic algorithm configuration (2016, unknown): “[13, 14, 15] have used it extensively as a benchmark for testing automatic tuning tools.”