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Call for Papers: Special Sessions

Special sessions are organized as part of LION4 as a way to focus submissions and encourage more interaction between specific communities. In general, submission and publication rules are the same as for the general call for papers, with the organizers of the special sessions coordinating and helping in identifying competent reviewers.

"Multiobjective Metaheuristics" (LION-MOME)

Organizers: Prof. David Wolfe Corne Heriot-Watt University, UK, Prof Qingfu Zhang University of Essex, UK, Dr. Dario Landa-Silva, University of Nottingham, UK, Dr. Hui Li, University of Nottingham, UK

Multiobjective optimization arises in many real-world applications. Very often, the objectives conflict with each other. There is no single solution which can optimize all the objectives simultaneously. Multiobjective metaheuristics are a basic tool for helping decision makers to find an optimal trade-off solution. These methods have attracted growing attention and become one of the hottest research topics in the area of the metaheuristics.

This special session aims at providing a forum for researchers to exchange ideas, identify future research directions and build research links.

Papers on all topics related to the session's theme are solicited. Prospective authors can submit papers via the online submission system of LION 2010. Authors are advised to be careful when selecting a paper category. The paper categories for LION-MOME are (1) LION-MOME: Regular Paper, (2) LION-MOME: Short paper, and (3) LION-MOME: Work for oral presentation only. In addition, an e-mail message including title of paper, author's names and affiliation, and abstract must be sent to the session chair: Prof Qingfu Zhang, e-mail: qzhang@essex.ac.uk.

All accepted novel and unpublished papers will be published in the post-conference proceedings of LION.

"Crossing the Chasm for Evolutionary Computation" (LION-CCEC)

Organizers: Gabriela Ochoa University of Nottingham, UK, and Marc Schoenauer, INRIA Saclay - Ile-de-France and Microsoft/INRIA joint center, Saclay, France.

Despite the success of evolutionary algorithms in solving difficult real-world optimization problems, their application to newly encountered problems, or even new instances of known problems remains problematic - even for experienced researchers of the field not to mention newcomers, or scientists and engineers from other areas. Theory and/or practical tools are still missing to make them "crossing the chasm" (Geoffrey A. Moore, 1991). The difficulties faced by the users arise mainly from the significant range of algorithm and/or parameter choices involved when using this type of approaches, and the lack of guidance as to how to proceed for selecting them. Moreover, state-of-the-art approaches for real-world problems tend to represent bespoke problem-specific methods which are expensive to develop and maintain. Relevant topics to this Special Session include off-line techniques (DOE, DACE, Racing, SPO, ...) as well as on-line adaptive and self-tuning algorithms and hybrid methods involving other meta-heuristics.

This special session aims at providing a forum for researchers to exchange ideas, identify future research directions and build research links.

Papers on all topics related to the session's theme are solicited. Prospective authors can submit papers via the online submission system of LION 2010. Authors are advised to be careful when selecting a paper category. The paper categories for LION-CCEC are (1) LION-CCEC: Regular Paper, (2) LION-CCEC: Short paper, and (3) LION-CCEC: Work for oral presentation only. In addition, an e-mail message including title of paper, author's names and affiliation, and abstract must be sent to the session chair: Prof Marc Schoenauer, e-mail: Marc.Schoenauer@lri.fr

All accepted novel and unpublished papers will be published in the post-conference proceedings of LION.

"Performance Prediction" (LION-PP)

Organizers: Professor Kate Smith-Miles Monash University, Australia

There is much that can be learned about the behavior of optimization algorithms on different classes of problem instances by studying meta-data. Such meta-data comprises large sets of diverse problem instances, together with measurable features of the instances, along with the performance results of a variety of algorithms. Machine learning methods can be used to learn the relationships in the meta-data to predict which algorithm is likely to perform best for a given problem instance, as well as to generate insights into why some algorithms are better suited to certain problem classes. Advances in the development of suitable features to characterize problem instances are needed, as well as novel approaches to learning the relationships in the meta-data.

This special session aims at providing a forum for researchers to exchange ideas, identify future research directions and build research links.

Papers on all topics related to the session's theme are solicited. Prospective authors can submit papers via the online submission system of LION 2010. Authors are advised to be careful when selecting a paper category. The paper categories for LION-PP are (1) LION-PP: Regular Paper, (2) LION-PP: Short paper, and (3) LION-PP: Work for oral presentation only. In addition, an e-mail message including title of paper, author's names and affiliation, and abstract must be sent to the session chair: Prof Kate Smith-Miles, e-mail: kate.smith-miles@sci.monash.edu.au.

All accepted novel and unpublished papers will be published in the post-conference proceedings of LION.

"Learning and Intelligent OptimizatioN in Structured domains" (LION-S)

Organizers: Prof Marcello Pelillo and Samuel Rota Bulò, Ca' Foscari University, Venice, Italy

In many interesting application areas data can naturally be abstracted in structured forms of varying sizes, e.g., sequences, strings, trees, graphs, etc. Traditionally, machine learning approaches working in structured domain attempt to obtain flat vectorial representations of the data in order to apply standard learning techniques for vector spaces. Such embeddings, however, may lead to a loss of relevant structural information. On the other hand, the development of learning approaches working directly in structured domain is challenging, as they may require solving hard optimization problems.

The goal of this special session is to consolidate research efforts in this area and to provide an informal discussion forum for researchers interested in this subject, both from a theoretical and practical perspective.

Papers on all topics related to the session's theme are solicited. Prospective authors can submit papers via the online submission system of LION 2010. Authors are advised to be careful when selecting a paper category. The paper categories for LION-S are (1) LION-S: Regular Paper, (2) LION-S: Short paper, and (3) LION-S: Work for oral presentation only. In addition, an e-mail message including title of paper, author's names and affiliation, and abstract must be sent to the session co-chair: Samuel Rota Bulò, e-mail: srotabul@dsi.unive.it.

All accepted novel and unpublished papers will be published in the post-conference proceedings of LION.

"Software for Optimization" (LION-SWOP)

Organizers: Mauro Brunato (University of Trento, Italy), Youssef Hamadi (Microsoft Research, Cambridge, UK), Silvia Poles (EnginSoft, Italy), Andrea Schaerf (University of Udine, Italy)

Optimization research benefits from potential applications in possibly any human endeavour: indeed, optimization is a very natural way to express a wide variety of problems. Moving optimization problems and algorithms out of academia and into the "real world", however, calls for joint efforts in different areas, such as the design of a sound formalism for the description of the systems being addressed; efficient implementations of algorithms for data sizes that were not considered in preliminary investigations; presentation of potentially large solution sets in a human-understandable way; integration of human expertise within the software; and so on.

Topics of interest to the special session include (but are not limited to) optimization package engineering, user interfaces for optimization software, integration of optimization libraries in vertical applications, problems with time-consuming functions and with a high number of objectives, optimization data formats, solver competitions.

Papers on all topics related to the session's theme are solicited. Prospective authors can submit papers via the online submission system of LION 2010. Authors are advised to be careful when selecting a paper category. The paper categories for LION-SWOP are (1) LION-SWOP: Regular Paper, (2) LION-SWOP: Short paper, and (3) LION-SWOP: Work for oral presentation only. In addition, an e-mail message including title of paper, author's names and affiliation, and abstract must be sent to Mauro Brunato, e-mail: brunato@disi.unitn.it.

All accepted novel and unpublished papers will be published in the post-conference proceedings of LION.