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ALLSTAT  March 2011

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Subject:

CFP - GECCO 2011 Workshop on Scaling Behaviours of Landscapes, Parameters and Algorithms

From:

Ender Ozcan <[log in to unmask]>

Reply-To:

Ender Ozcan <[log in to unmask]>

Date:

Thu, 3 Mar 2011 17:02:35 -0000

Content-Type:

text/plain

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text/plain (102 lines)

[Please circulate to all those who might be interested, and accept our
apologies if you received multiple copies of this announcement]

************************************************************
                    CALL FOR PAPERS

                      Workshop on
Scaling Behaviours of Landscapes, Parameters and Algorithms
      www.cs.nott.ac.uk/~ajp/GECCO-2011-HU-workshop/

                 to be held as part of the

    2011 Genetic And Evolutionary Computation Conference
                    (GECCO-2011)

                  July 12-16, 2011
                  Dublin, Ireland
              Organized by ACM SIGEVO
             www.sigevo.org/gecco-2011/

         Workshop Paper Submission Deadline:
                    April 7, 2011
************************************************************

Description:
All too often heuristics and meta-heuristics 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
GECCO 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.

We invite submissions as extended abstracts of around 3-4 pages addressing a
relevant topic. Speculative or position papers also are welcome. Submissions
will be reviewed for quality and relevance. Papers up to the usual limit of
8 pages are also permitted, however we will prefer extended abstracts.
Furthermore, longer papers will not be given a longer time for potential
presentations. See the workshop page for the submission guidelines and more.

Workshop Organisers:
Ender Ozcan, Andrew J. Parkes and Jon Rowe
   www.cs.nott.ac.uk/~exo/
   www.cs.nott.ac.uk/~ajp/
   www.cs.bham.ac.uk/~jer/

Important Dates:
   - Deadline for abstract submission: 7 April
   - Notification of Acceptance:      14 April
   - Camera-ready deadline:           26 April
   - Registration deadline:            2 May

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