Technical program
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Special Sessions
Program Committee

LION 8 Call for Papers: Special Sessions

Special sessions are organized as part of LION8 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.

This webpage will be updated as more sessions are proposed and accepted. In case you are interested in organizing a session during LION 8, please feel free to contact us at lion8conference(at)gmail(dot)com.

Algorithm Selection and Portfolio Techniques (LION-PORTFOLIO)

  • Bernd Bischl, Research Associate at the Statistics Faculty, TU Dortmund (Germany)
  • Heike Trautmann, Professor for Information Systems and Statistics at M√ľnster University (Germany)
  • Holger Hoos, Professor for Computer Science at the University of British Columbia (Canada)
All three organizers are members of the Configuration and Selection of Algorithms (COSEAL) research group.

Algorithm selection methods have been studied since the 1970s. Within the last 20 years, these and related portfolio-based techniques (such as parallel algorithm portfolios, sequential algorithm schedules and hybrid forms) have been rapidly gaining popularity for challenging continuous and discrete problems. These techniques now form the basis for state-of-the-art algorithms for many computationally challenging problems, such as propositional satisfiability (SAT), continuous black-box function optimization, AI planning and meta-learning. This special track aims to showcase novel contributions to the broad topic of algorithm selection and portfolio techniques. Topics of interest include, but are not limited to, the following:

  • per-instance algorithm selection
  • static (per-class) algorithm selection
  • parallel algorithm portfolios
  • static and dynamic sequential algorithm schedules
  • dynamic algorithm selection via reinforcement learning techniques
  • machine learning models and statistical methods for algorithm selection
  • problem instance features / exploratory landscape analysis
  • empirical assessment and theoretical properties of algorithm selection and portfolio techniques
  • applications to problems of industrial or academic relevance
  • generation of benchmarks

Market Network Analysis (LION-MARKET)

  • Valery Kalyagin, Laboratory of Algorithms and Technologies for Networks Analysis, Russia

Network approach for eco systems analysis has attracted important attention during the last decades. One of the popular topics of research in this area is market network analysis. The importance of market network analysis is supported by the growing number of publications related with it. The purpose of this session is to bring together scientists and practitioners to exchange knowledge and results in a broad range of topics relevant to the theory and practice of market network analysis. The session will focus on the theoretical basis for the market network analysis practice, open problems and new phenomena discovered on the market by these tools.

Intelligent optimization in Bioinformatics, Biomedecine and Neuroscience (LION-BIO)

  • Clarisse Dhaenens, University of Lille 1, INRIA Lille
  • Laetitia Jourdan, University of Lille 1, INRIA Lille

Bioinformatics, Biomedecine and Neuroscience represent a great challenge for optimization methods as many problems arizing in these fields 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. In biomedecine (or medical biology), such optimization problems may also be found by studying molecular interactions. Solving such difficult combinatorial optimization 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 problems coming from bioinformatics, biomedecine and neuroscience.

Topics of interest include, but are not limited to:
Original modeling and solving Bioinformatics, Biomedecine and Neuroscience optimization problems (for example: Folding, docking, protein interaction, network inference etc.) Metaheuristics to solve knowledge discovery problems encountered in problems from these fields, such as classification, clustering, association rules, feature selection... Knowledge discovery approaches embedded in metaheuristics to incorporate knowledge about the problem to be solved

Cluster analysis in networks (LION-GRAPH)

  • Sergiy Butenko, Texas A&M

Many important complex systems of diverse origins can be conveniently modeled using networks. Cluster analysis of such networks is useful in characterizing local and global structural properties of the underlying complex systems. Choosing an appropriate definition of a cluster is one of the key considerations when performing cluster analysis. This session will focus on discussing recent progress in studying a class of cluster models referred to as clique relaxations, arising in computational biology, social network analysis, and telecommunication systems among numerous other applications.