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LION6 Call for Papers: Special Sessions

Special sessions are organized as part of LION6 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.

Get the list of LION 6 special sessions in pdf, ready to print.

"Autonomous Control for Search Algorithms" (LION-S*EA)

Organizers: Frédéric Lardeux, University of Angers, France, Frédéric Saubion , University of Angers, France, Sébastien Verel, University of Sophia-Antipolis, France

Since recent years, impressive progresses have been achieved to improve parameter tuning for search algorithms. In the context of problem solving (e.g., optimization problems or constraint satisfaction problems), parameter setting is indeed of interest for different computer science communities such as numerical optimization, operation research or constraint programming. LION is thus the ideal conference for researchers from these communities to meet and present their results on that topic. Among different approaches, one may usually distinguish between tuning parameter before running the algorithm, e.g., by learning from previous observations, and controlling the parameters during the run, e.g. by analyzing the current state of the search process. Focusing on this last aspect, we propose to organize this special session devoted to autonomous control techniques (we use the word autonomous in order to gather different existing taxonomies). The general purpose of control is to provide more autonomous algorithms whose behaviour is dynamically adjusted with regards to the solving goal to achieve. Reinforcement leaning techniques may then be used to benefit from the information collected along the search. Of course many other control methods can be considered, including the use of external knowledge. Let us note that the notion of parameter includes classic numerical parameters that manage the general behaviour of the algorithms but also structural parameters that are more related to the design of the algorithm itself, such as basic search heuristics or neighbourhood functions. The main motivation of this session will be to present a panel of control techniques for different solving paradigms. We will attempt to exhibit the common or the complementary aspects of existing control approaches. To this aim, our session would address the following topics (not limited to):

  • Adaptive parameter control in evolutionary algorithms
  • Reactive prohibition mechanisms in local search algorithms
  • Adaptive multiple neighbourhood management in local search algorithms
  • Heuristics control in hyper heuristics based algorithms
  • Control for parallel and distributed algorithms (parallel constraint solvers, island models...)
  • Dynamic heuristics selection in tree based constraint solvers
Scope and audience : The scope of the session seems large enough to gather works from operation research, constraint programming and evolutionary computation communities (including all kind of meta-heuristics based algorithms). Nevertheless, the scope is also specific enough to insure an interesting session and to promote discussions between researchers.

Papers on all topics related to the session's themes are solicited. Prospective authors should submit papers via the online submission system of LION 6. Authors are advised to be careful when selecting a paper category. The paper categories for LION-S*EA are (1) LION-S*EA: Regular Paper, (2) LION-S*EA: Short paper, and (3) LION-S*EA: Work for oral presentation only. In addition, an e-mail message including paper Id, title of paper, author's names, and abstract must be sent to: Frederic.Saubion[[at]]univ-angers.fr

All accepted novel and unpublished papers will be published in the post-conference proceedings of LION6.

"MaxSAT: Algorithms and Applications" (LION-MAXSAT)

Organizers: Chu Min Li (Université de Picardie Jules Verne) and Felip Manyà (Artificial Intelligence Research Institute, IIIA-CSIC)

The MaxSAT problem has been actively investigated by different research communities, including Artificial Intelligence, Operations Research, and Theoretical Computer Science, and even a MaxSAT solver competition has been held annually since 2006. The emphasis placed on MaxSAT depends on the community, and ranges from the development of heuristic algorithms to the implementation of exact solvers, from proving properties of approximation algorithms to proving completeness of MaxSAT inference systems, from devising clever MaxSAT encodings of academic problems to solve practical optimization problems, ... The challenge now is to use all the existing results to build a highly competitive generic approach to solving hard optimization problems. This special session aims at providing a forum to present original contributions on all aspects of MaxSAT, and identify promising research directions. It is an opportunity to exchange ideas among people working on MaxSAT from different perspectives.

Topics of interest to the special session include (but are not limited to) problem modeling, solving algorithms, combining/integrating different frameworks and algorithms, comparative studies, solver competitions, and interesting and innovative applications.

Papers on all topics related to the session's themes are solicited. Prospective authors should submit papers via the online submission system of LION 6. Authors are advised to be careful when selecting a paper category. The paper categories for LION-MAXSAT are (1) LION-MAXSAT: Regular Paper, (2) LION-MAXSAT: Short paper, and (3) LION-MAXSAT: Work for oral presentation only. In addition, an e-mail message including paper Id, title of paper, author's names, and abstract must be sent to: felip[[at]]iiia.csic.es

All accepted novel and unpublished papers will be published in the post-conference proceedings of LION6.

"Cross-domain Heuristic Search" (LION-CHESC)

Organizers: Gabriela Ochoa and Mathew Hyde , University of Nottingham, UK,

Automating the design of heuristic search methods is an active research field within computer science, artificial intelligence and operational research. An underlying research challenge is to develop more generally applicable search methodologies. However, researchers developing such methodologies are often constrained on the number of problem domains on which to test their adaptive, self-configuring algorithms. This can be explained by the inherent difficulty of implementing the corresponding domain specific software components. Within the 'Automated Scheduling Optimisation and Planning' (ASAP) group, at the University of Nottingham we have proposed and implemented HyFlex (http://www.asap.cs.nott.ac.uk/hyflex/), a software framework for the development of cross-domain search methodologies. The framework can be considered as a benchmark for heuristic search generality. It features a common software interface and provides the problem specific components for six hard combinatorial optimisation problems: maximum satisfiability, one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing. The framework formed the basis for the first International Cross-domain Heuristic Search Challenge (CHeSC) (http://www.asap.cs.nott.ac.uk/chesc2011/).

This special session provides a forum for researchers using the HyFlex framework to exchange ideas, identify useful design principles, and showcase their cross-domain algorithms. We also welcome submission not using the framework, but that develop approaches which are general across several problem domains.

Prospective authors should submit papers via the online submission system of LION 2012. Authors are advised to be careful when selecting a paper category. The paper categories for LION-CHESC are (1) LION-CHESC: Regular Paper, (2) LION-CHESC: Short paper, and (3) LION-CHESC: Work for oral presentation only. In addition, an e-mail message including paper Id, title of paper, author's names, and abstract must be sent to: chesc[at]cs[dot]nott[dot]ac[dot]uk.

All accepted novel and unpublished papers will be published in the post-conference proceedings of LION6.

"Large Scale Parallelism in Search" (LION-Par)

Organizers: Philippe Codognet (CNRS / UPMC / University of Tokyo, France/Japan), Pierre Collet (University of Strasbourg, France), Florian Richoux (CNRS / University of Tokyo, France/Japan).

With the development of multi-core workstations, the availability of GPGPU-enhanced systems, and the access to Grid platforms or supercomputers worldwide, parallel programming is appearing in many domains as a key issue in order to use the computing power at hand in an efficient manner. Heuristic algorithms for hard optimization problems are not isolated from this phenomenon, as bigger computing power means the ability to attack (and solve) more complex problems. Many intelligent optimization algorithms are inherently parallel. In the last decade, various experiments have been done to extend different types of search algorithms (metaheuristics and local search, constraint solvers, SAT solvers, branch & bound) for parallel execution, but most of the time on shared memory multi-core systems (a few cores) or small PC clusters (a few machines or a few tens of machines). The next challenge is thus to devise efficient heuristic search and optimization algorithms for massively parallel computers and heterogeneous systems that will be both scalar and GPU-based and to show their efficiency on very hard optimization problems.

The aim of this special session is to receive papers in the topic of the LION conference (metaheuristics, local search, tabu search, evolutionary algorithms, ant colony optimization, particle swarm optimization, memetic algorithms, and other types of search algorithms) implemented on all kinds of parallel hardware: scalar, GPU-based or heterogeneous massively parallel systems. This workshop is designed to be a forum for researchers willing to tackle those issues, in order to exchange theoretical algorithms and methods, implementation designs, experimental results, to identify future research directions and to further boost this growing area through cross-fertilization.

Papers on all topics related to the session's theme are solicited, in particular: parallelization of existing search algorithms and new parallel methods; heuristic algorithms and combinatorial optimization on Grids, large PC clusters, massively parallel computers, and GPUs; adaptive strategies and learning for parallel search and optimization; applications and benchmarking; theoretical studies and complexity.

Prospective authors can submit papers via the online submission system of LION 6. Authors are advised to be careful when selecting a paper category. The paper categories for LION-Par are (1) LION-Par: Regular Paper, (2) LION-Par: Short paper, and (3) LION-Par: 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 organizers: codognet [AT] jfli.itc.u-tokyo.ac.jp, pierre.collet [AT] unistra.fr, richoux [AT] jfli.itc.u-tokyo.ac.jp

All accepted novel and unpublished papers will be published in the post-conference proceedings of LION6.

"Intelligent optimization in Bioinformatics" (LION-BIO)

Organizers: Clarisse Dhaenens, University of Lille 1, INRIA Lille, Laetitia Jourdan, University of Lille 1, INRIA Lille Nord Europe

Bioinformatics represents a great challenge for optimization methods as many bioinformatics problems can be modelized as large size optimization problems. For example, many bioinformatics problems deal with the manipulation of large sets of variables (SNPs, genes, GWA, proteins ...). Hence, looking for a good combination of these variables require advance search mechanisms. Solving such difficult problems require to incorporate knowledge about problems to be solved. This special session aims at putting together works in which optimization approaches and knowledge discovery are jointly concerned to solve bioinformatics problems.

Topics of interest include, but are not limited to:

  • Original modeling and solving of bioinformatics optimization problems (for example: Folding, docking, protein interaction, network inference etc.)
  • Metaheuristics to solve knowledge discovery problems encountered in bioinformatics problems, such as classification, clustering, association rules, feature selection...
  • Knowledge discovery approaches embedded in metaheuristics to incorporate knowledge about the problem to be solved -...
Papers on all topics related to the session's themes are solicited.

Prospective authors should submit papers via the online submission system of LION 6. Authors are advised to be careful when selecting a paper category. The paper categories for LION-BIO are (1) LION-BIO: Regular Paper, (2) LION-BIO: Short paper, and (3) LION-BIO: Work for oral presentation only. In addition, an e-mail message including paper Id, title of paper, author's names, and abstract must be sent to: laetitia.jourdan[[at]]lifl.fr

All accepted novel and unpublished papers will be published in the post-conference proceedings of LION6.

Dynamic optimization (LION-DO)

Organizers: Patrick Siarry (siarry@u-pec.fr) and Amir Nakib (nakib@u-pec.fr) , University of Paris-Est Créteil, France (Laboratoire Images, Signaux et Systèmes Intelligents, LiSSi).

Recently, optimization in dynamic environments has attracted a growing interest, due to its practical relevance. Many real-world problems are dynamic optimization problems, i.e. their objective function changes over time: typical examples are in dynamic vehicle routing, inventory management and scheduling, object tracking. Sometimes, the objective function is uncertain or noisy as a result of simulation/measurement errors or approximation errors. In addition, the design variables or environmental conditions may also be perturbed or change over time.

For these dynamic and uncertain optimization problems, the objective of an efficient metaheuristic is to locate the global optimum solution, and to continuously track the optimum in dynamic environments, or to find a robust solution that operates optimally in the presence of uncertainties. This special session aims at bringing academic researchers and industrials together to review the latest advances and explore future directions in this field. Topics of interest include but are not limited to:

  • Benchmark problems and performance measures
  • Tracking moving optima
  • Dynamic multiobjective optimization
  • Adaptation, learning, and anticipation
  • Handling noisy fitness functions
  • Using fitness approximations
  • Searching for robust optimal solutions
  • Comparative studies
  • Hybrid approaches
  • Theoretical analysis
  • Real-world applications
Papers on all topics related to the session's themes are solicited. Prospective authors should submit papers via the online submission system of LION6. Authors are advised to be careful when selecting a paper category. The paper categories for LION-DO are (1) LION-DO: Regular Paper, (2) LION-DO: Short paper, and (3) LION-DO: Work for oral presentation only. In addition, an e-mail message including paper Id, title of paper, authors' names, and abstract must be sent to: nakib[[at]]u-pec.fr or siarry[[at]]u-pec.fr

All accepted novel and unpublished papers will be published in the post-conference proceedings of LION6.