[Please circulate to all those who might be interested, and accept our apologies if you received multiple copies of this announcement]
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CALL FOR ABSTRACTS
Joint Workshop on
Automated Selection and Tuning of Algorithms Part B
Discrete Search Spaces — Focus on Parameter Selection
to be held as part of the
12th Int. Conf. on Parallel Problem Solving from Nature
(PPSN 2012)
September 1-5, 2012
Taormina, Italy
Workshop Abstract Submission Deadline:
June 22, 2012
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We invite abstract submissions (of around 1-2 pages) addressing a relevant topic. These submissions will be used to determine whether the associated presentation is suitable for inclusion. Hence, they can be informal. A brief summary of abstracts/presentations will be placed on the workshop webpage. Speculative, position or provocative presentations are welcome. The provisional duration for a presentation is 20-30 minutes, including questions and discussions. Please submit your abstract to Ender Ozcan (ender.ozcan [ATT] nottingham.ac.uk) and Andrew J. Parkes (andrew.parkes [ATT] nottingham.ac.uk) by email and do not hesitate to contact us if you have any queries or requests.
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Description:
All too often heuristics and meta-heuristics for discrete combinatorial optimisation problems require significant parameter tuning to work most effectively. Often this tuning is performed without any a priori knowledge as to how good values of parameters might depend on features of the problem. This lack of knowledge can lead to lot of computational effort and also has the danger of being limited to only problem instances that are similar to those that have been seen before. The aim of the workshop is to develop methods to give deeper insight into problem classes, and how to obtain and exploit structural information. In particular, we often would like to be able to tune parameters using small instances (for speed) but then adjust so as to be able to run on large instances. This will require some theory of how to extrapolate tuning outside of the size or features of the training set. An analogy is the difference between non-parametric and parametric statistics; the former does not assume any underlying probability distribution and the latter can (for example) assume a Gaussian. Naturally, the latter might give stronger results and with smaller sized samples. Hence, to distinguish this from standard parameter tuning, we might call this “Parametric Parameter Tuning”. Of course, this is a challenging problem; but we hope to be able to discuss any existing work and how the community might meet the challenge.
Related to this is the common and natural belief that the semantic properties of the landscapes will be reflected in the performances of algorithms. A subsequent underlying assumption, or hypothesis, if the landscape has a particular functional dependence on features of the instance, then such functional dependencies are also likely to play a key role in understanding the behaviour of heuristic algorithms, and so merit investigation. We are particularly interested the area of phase transitions; when particular semantic properties display phases of 'almost always true' and 'almost never true'. Statistical methods can then reveal some appropriate parameters to describe the locations of such phases, and we expect that this will also influence the understanding, design and tuning of algorithms. This is exemplified by the work in the artificial intelligence and statistical physics communities on propositional satisfiability and graph colouring, and that has led to deeper understanding of algorithms, and development of new ones. One of the goals of the workshop is to look into phase transition theory with a view to potential applications to traditional PPSN problems.
The target participants are those that:
* Work on the theory of search algorithms, but are seeking ways for the theory to have a practical impact
* Work on direct applications, but are frustrated with the trial-and-error approaches that often are often used, and would like to bring -theoretically-inspired methods- into their work.
We also aim to bring together researchers and practitioners from related fields such as Operational Research (OR), Artificial Intelligence and Computer Science, providing a medium for sharing and inspiring of techniques (even if application domains are different) and developing common understandings.
Workshop Organisers:
Ender Ozcan and Andrew J. Parkes
Important Dates:
- Deadline for abstract submission: 22 June, 2012
- Notification of Acceptance: 22 June, 2012
- Early registration deadline: 25 June, 2012