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Detailed Programme

Computational Intelligence for Risk Management, Security and Defence Applications

Recent events involving both natural disasters and human-made attacks have emphasised the importance of solving challenging problems in risk management, security and defence. Traditional methods have proven insufficient to address these problems and hence Computational Intelligence techniques present themselves as a more appealing alternative.

Areas of Interest and Contributions

We seek both theoretical developments and applications of Computational Intelligence to these subjects. All bio-inspired computational paradigms are welcome, mainly Genetic and Evolutionary Computation, but also Fuzzy Logic, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and others, including hybrids.
Topics include but are not limited to:


Accepted papers will appear in the proceedings of EvoStar, published in a volume of the Springer Lecture Notes in Computer Science, which will be available at the Conference.Submissions must be original and not published elsewhere. The submissions will be peer reviewed by at least three members of the program committee. The authors of accepted papers will have to improve their paper on the basis of the reviewers comments and will be asked to send a camera ready version of their manuscripts. At least one author of each accepted work has to register for the conference and attend the conference and present the work.The reviewing process will be double-blind, please omit information about the authors in the submitted paper.

Submission Details

Submissions must be original and not published elsewhere. They will be peer reviewed by members of the program committee. The reviewing process will be double-blind, so please omit information about the authors in the submitted paper.

Submit your manuscript in Springer LNCS format.

Please provide up to five keywords in your Abstract

Page limit: 12 pages to http://myreview.csregistry.org/evoapps14/.


Submission deadline: 1 November 2013 11 November 2013
Notification: 06 January 2014
Camera ready: 01 February 2014
EvoRISK: 23-25 April 2014


Further information on the conference and co-located events can be
found in: http://www.evostar.org

Programme Committee

EvoRISK Programme

Fri 1000-1140  EvoINDUSTRY & EvoRISK
Chairs: Kevin Sim & Anna I Esparcia-Alcázar

Reducing the Number of Simulations in Operation Strategy Optimization for Hybrid Electric Vehicles     Christopher Bacher, Thorsten Krenek, Günther Raidl
The fuel consumption of a simulation model of a real Hybrid Electric Vehicle is optimized on a standardized driving cycle using metaheuristics (PSO, ES, GA). Search space discretization and metamodels are considered for reducing the number of required, time-expensive simulations. Two hybrid metaheuristics for combining the discussed methods are presented. In experiments it is shown that the use of hybrid metaheuristics with discretization and metamodels can lower the number of required simulations without significant loss in solution quality.

Hybridisation Schemes for Communication Satellite Payload Configuration Optimisation     Apostolos Stathatkis, Gregoire Danoy, El-Ghazali Talbi, Pascal Bouvry, Gianluigi Morelli
The increasing complexity of current telecommunication satellite payloads has made their manual management a difficult and error prone task. As a consequence, efficient optimisation techniques are required to help engineers to configure and reconfigure the payload. Recent works focusing on exact approaches faced scalability issues while metaheuristics provided unsatisfactory solution quality. This work therefore proposes three hybridisation schemes that combine both metaheuristics and an exact method. Experimental results on realistic payload sizes demonstrate the advantage of those approaches in terms of efficiency and scalability within a strict operational time constraint of ten minutes on a single CPU core.

Hyper-Heuristics for Online UAV Path Planning under Imperfect Information     Engin Akar, Haluk Topcuoglu, Murat Ermis
Hyper-heuristic techniques are problem independent meta-heuristics that automate the process of selecting a set of given low-level heuristics. Online path planning in an uncertain or unknown environment is one of the challenging problems for autonomous unmanned aerial vehicles (UAVs). This paper presents a hyper-heuristic approach to develop a 3-D online path planning for unmanned aerial vehicle (UAV) navigation under sensing uncertainty. The information regarding the state of a UAV is obtained from on-board sensors during the execution of a navigation plan. The trajectory of a UAV at each region is represented with B-spline curves, which is constructed by a set of dynamic control points. Experimental study performed on various terrains with different characteristics validates the usage of hyper-heuristics for online path planning. Our approach outperforms related work with respect to the quality of solutions and the number of feasible solutions produced.