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Viz: a visual analysis suite for explaining local search behavior
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Source Symposium on User Interface Software and Technology archive
Proceedings of the 19th annual ACM symposium on User interface software and technology table of contents
Montreux, Switzerland
SESSION: Information landscapes table of contents
Pages: 57 - 66  
Year of Publication: 2006
ISBN:1-59593-313-1
Authors
Steven Halim  National University of Singapore
Roland H. C. Yap  National University of Singapore
Hoong Chuin Lau  Singapore Management University
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
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APPENDICES and SUPPLEMENTS
Zipp57-slides.zip (24.16 MB),
Supplemental material for Viz: a visual analysis suite for explaining local search behavior


ABSTRACT

NP-hard combinatorial optimization problems are common in real life. Due to their intractability, local search algorithms are often used to solve such problems. Since these algorithms are heuristic-based, it is hard to understand how to improve or tune them. We propose an interactive visualization tool, VIZ, meant for understanding the behavior of local search. VIZ uses animation of abstract search trajectories with other visualizations which are also animated in a VCR-like fashion to graphically playback the algorithm behavior. It combines generic visualizations applicable on arbitrary algorithms with algorithm and problem specific visualizations. We use a variety of techniques such as alpha blending to reduce visual clutter and to smooth animation, highlights and shading, automatically generated index points for playback, and visual comparison of two algorithms. The use of multiple viewpoints can be an effective way of understanding search behavior and highlight algorithm behavior which might otherwise be hidden.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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B. Adenso-Diaz and M. Laguna. Fine-tuning of Algorithms Using Fractional Experimental Designs and Local Search. Operations Research, 54(1):99--114, 2006.
 
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S. Halim and H. C. Lau. Tuning Tabu Search Strategies via Visual Diagnosis. To appear in MIC2005 Post-Conf. Volume. Kluwer Academic Press, 2007.
 
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Collaborative Colleagues:
Steven Halim: colleagues
Roland H. C. Yap: colleagues
Hoong Chuin Lau: colleagues