Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Viz: a visual analysis suite for explaining local search behavior
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Stochastic Optimization (Scientific Computation)
Stochastic Optimization (Scientific Computation)
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Designing and tuning SLS through animation and graphics: an extended walk-through
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
Performance prediction and automated tuning of randomized and parametric algorithms
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Engineering Stochastic Local Search for the Low Autocorrelation Binary Sequence Problem
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Instance-Based parameter tuning via search trajectory similarity clustering
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Fine-Tuning algorithm parameters using the design of experiments approach
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms
Journal of Heuristics
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Stochastic Local Search (SLS) is a simple and effective paradigm for attacking a variety of Combinatorial (Optimization) Problems (COP). However, it is often non-trivial to get good results from an SLS; the designer of an SLS needs to undertake a laborious and ad-hoc algorithm tuning and re-design process for a particular COP. There are two general approaches. Black-box approach treats the SLS as a black-box in tuning the SLS parameters. White-box approach takes advantage of humans to observe the SLS in the tuning and SLS re-design. In this paper, we develop an integrated white+black box approach with extensive use of visualization (white-box) and factorial design (black-box) for tuning, and more importantly, for designing arbitrary SLS algorithms. Our integrated approach combines the strengths of white-box and black-box approaches and produces better results than either alone. We demonstrate an effective tool using the integrated white+black box approach to design and tune variants of Robust Tabu Search (Ro-TS) for Quadratic Assignment Problem (QAP).