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|Title:||An integrated white+black box approach for designing and tuning stochastic local search|
|Authors:||Halim, S. |
|Citation:||Halim, S.,Yap, R.H.C.,Lau, H.C. (2007). An integrated white+black box approach for designing and tuning stochastic local search. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4741 LNCS : 332-347. ScholarBank@NUS Repository.|
|Abstract:||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). © Springer-Verlag Berlin Heidelberg 2007.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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