A Hyperheuristic Approach for Guiding Enumeration in Constraint Solving

@inproceedings{Crawford2012AHA,
  title={A Hyperheuristic Approach for Guiding Enumeration in Constraint Solving},
  author={Broderick Crawford and Carlos Castro and {\'E}ric Monfroy and Ricardo Soto and Wenceslao Palma and Fernando Paredes},
  booktitle={EVOLVE},
  year={2012}
}
In this paper we design and evaluate a dynamic selection mechanism of enumeration strategies based on the information of the solving process. Unlike previous research works we focus in reacting on the fly, allowing an early replacement of bad-performance strategies without waiting the entire solution process or an exhaustive analysis of a given class of problems. Our approach uses a hyperheuristic approach that operates at a higher level of abstraction than the Constraint Satisfaction Problems… 
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References

SHOWING 1-10 OF 22 REFERENCES
A Hyperheuristic Approach to Scheduling a Sales Summit
TLDR
The behaviour of several different hyperheuristic approaches for a real-world personnel scheduling problem is analysed and the effectiveness of this approach is shown and wider applicability of hyper heuristic approaches to other problems of scheduling and combinatorial optimisation is suggested.
Using a Choice Function for Guiding Enumeration in Constraint Solving
TLDR
A Choice Function for guiding enumeration is proposed, which provides guidance to the solver by indicating which enumeration strategy should be applied next based upon the information of the search process.
Practice and Theory of Automated Timetabling III
TLDR
A multiobjective genetic algorithm was proposed for this timetabling problem, incorporating two distinct objectives that concern precisely the minimization of the violations of both types of constraints, hard and soft, while respecting the two competing aspects of teachers and classes.
Adaptive Constraint Satisfaction: The Quickest First Principle
TLDR
This report describes one such adaptive algorithm, based on the principle of chaining, designed to avoid the phenomenon of exceptionally hard problem instances and shows how the speed of more naive algorithms can be utilised safe in the knowledge that the exceptional behaviour can be bounded.
Adaptive Enumeration Strategies and Metabacktracks for Constraint Solving
TLDR
This paper design and evaluate strategies to improve resolution performances of a set of problems, and experimental results show the effectiveness of the approach.
Metaheuristics - From Design to Implementation
TLDR
This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling.
Autonomous Search
TLDR
This is the first book dedicated to autonomous search, and it can be used as a reference for researchers, engineers, and postgraduates in the areas of constraint programming, machine learning, evolutionary computing, and feedback control theory.
An Approach for Dynamic Split Strategies in Constraint Solving
TLDR
This work proposes to dynamically change strategies showing bad performances when this is not enough to improve resolution, and introduces some meta-backtracks to get good performances without the knowledge of experts.
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