Technical Program Committee

Steering Committee

Important Dates

Paper Submission Due:

December 18, 2015
23:59 GMT+1


Rebuttal open and close date:

February 24 - 28, 2016


Notification:

March 25, 2016
23:59 GMT+1


Call For Papers

LION 10 Call for Papers: Special Sessions

Special sessions are organized as part of LION 10 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.

Algorithm Configuration, Algorithm Selection, and Performance prediction

Organizers:

Bernd Bischl, Professor for Computational Statistics at LMU Munich (Germany);

Holger Hoos, Professor for Computer Science at the University of British Columbia (Canada);

Heike Trautmann, Professor for Information Systems and Statistics at Münster University (Germany).

All three organizers are members of the Configuration and Selection of Algorithms (COSEAL) Research Group.

Over the past decade, there has been substantial interest and progress in general-purpose automated algorithm selection, algorithm configuration and performance prediction methods and their application to a wide range of applications domains.

Prominent examples are continuous and discrete combinatorial optimization and model selection in machine learning.

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 as well as hyperparameter optimization.

This special session aims to showcase novel contributions to the broad topic of algorithm selection, algorithm configuration and performance prediction. Specifically, it will bring together researchers across multiple areas within computer science, operations research, statistics and beyond, particularly experts in machine learning and optimization, facilitating a fruitful exchange of ideas and recent results.

Topics of interest include, but are not limited to, the following:

Submissions to this special session will be subject to the same, rigorous peer-reviewing process as all other submissions to LION 10.

GENOPT Generalization-based contest in global optimization

Organizers:

Roberto Battiti, Head of LIONlab for "Machine Learning and Intelligent Optimization", University of Trento (Italy);

Yaroslav Sergeyev, Head of Numerical Calculus Laboratory, DIMES, University of Calabria (Italy);

Mauro Brunato, LIONlab, University of Trento (Italy);

Dmitri Kvasov, DIMES, University of Calabria (Italy).

While comparing results on benchmark functions is a widely used practice to demonstrate the competitiveness of global optimization algorithms, fixed benchmarks can lead to a negative data mining process. The motivated researcher can "persecute" the algorithm choices and parameters until the final designed algorithm "confesses" positive results for the specific benchmark.

With a similar goal, to avoid the negative data mining effect, the GENOPT contest will be based on randomized function generators, with fixed statistical characteristics but individual variation of the generated instances.

The generators will be made available to the participants to test offline and online tuning schemes, but the final competition will be based on random seeds communicated in the last phase.

A dashboard will reflect the current ranking of the participants, who are encouraged to exchange preliminary results and opinions.

The final "generalization" ranking will be confirmed in the last competition phase.

Tentative schedule:

LION10 conference: 29 May - 1 June, 2016
Reviewed and accepted papers are presented
Competition winners are publicly recognized

After LION: special issue of good-quality journal dedicated to results obtained by the Winning and reviewed papers.
For further information, visit http://www.genopt.org/

Intelligent optimization in Health, e-Health, Bioinformatics, Biomedecine and Neurosciences

Organizers:

Clarisse Dhaenens, University of Lille 1, INRIA Lille, (France);

Laetitia Jourdan, University of Lille 1, INRIA Lille, (France);

Frédéric Saubion, University of Angers, (France).

This special session aims at putting together works in which optimization approaches and knowledge discovery are jointly concerned to solve problems coming from Health, e-Health, Bioinformatics, biomedecine and neuroscience. The term e-Health or health informatics can be defined as healthcare process supported by electronic processes and communication.

Health, e-Health, 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. In e-Health a wide range of the services or systems are encountered and are at the edge of medicine and information technology. These services include, but not limited to: electronic health records, telemedicine, the use of mobile devices in collecting health data and providing real-time patient monitoring, healthcare information systems and intelligent medical diagnostic systems. The data provides by such devices are huge and the problems generated required efficient methods. Solving such difficult combinatorial optimization problems require to incorporate knowledge about problems to be solved.

Topics of interest include, but are not limited to: