Automatically improving the anytime behaviour of optimisation algorithms

DOI10.1016/j.ejor.2013.10.043zbMath1401.90274OpenAlexW2081453109MaRDI QIDQ2256321

Thomas Stützle, Manuel López-Ibáñez

Publication date: 19 February 2015

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2013.10.043

zbMATH Keywords

metaheuristicsanytime algorithmsautomatic configurationoffline tuning

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Mixed integer programming (90C11) Approximation methods and heuristics in mathematical programming (90C59) General topics in the theory of algorithms (68W01)

Related Items

Anytime Pareto local search, Capping methods for the automatic configuration of optimization algorithms, Recent advances in selection hyper-heuristics, MPILS: an automatic tuner for MILP solvers, Analysis of stochastic local search methods for the unrelated parallel machine scheduling problem, Effective anytime algorithm for multiobjective combinatorial optimization problems, Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio, Revisiting simulated annealing: a component-based analysis, Automatically improving the anytime behaviour of optimisation algorithms, A model of anytime algorithm performance for bi-objective optimization

Uses Software

• Paramils
• SCIP
• SPOT
• irace
• DIMACS
• ACOTSP
• SATenstein
• SMAC
• weightedHypervolume

Cites Work

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• ParamILS: An Automatic Algorithm Configuration Framework
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