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:
- Cyber crime: anomaly detection systems, attack prevention and defence, threat forecasting systems, anti spam, antivirus systems, cyber warfare, cyber fraud
- IT Security: Intrusion detection, behaviour monitoring, network traffic analysis
- Resilient and self-healing systems
- Risk management: identification, prevention, monitoring and handling of risks, risk impact and probability estimation systems, contingency plans, real time risk management
- Critical Infrastructure Protection (CIP)
- Military, counter-terrorism and other defence related aspects
- Disaster relief and humanitarian logistics
- Real-world applications of these subjects
PUBLICATION DETAILS
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/.
IMPORTANT DATES
Submission deadline: 1 November 2013 11 November 2013
Notification: 06 January 2014
Camera ready: 01 February 2014
EvoRISK: 23-25 April 2014
FURTHER INFORMATION
Further information on the conference and co-located events can be
found in: http://www.evostar.org
EvoRISK track chairs
- Anna I Esparcia-Alcázar
S2 Grupo, Spain
aesparcia(at)s2grupo.es - Nur Zincir-Heywood
Dalhousie University, Canada
zincir(at)cs.dal.ca
Programme Committee
- Abbass Hussein UNSW@Australian Defence Force Academy, Australia
- Abercrombie Robert K. Oak Ridge National Laboratory, USA
- Abielmona Rami University of Ottawa, Canada
- Abou El Kalam Anas École Nationale Supérieure d'Ingénieurs de Bourges, France
- Chaki Nabendu University of Calcutta, India
- Cococcioni Mario NATO Undersea Research Centre, Italy
- Domingo-Ferrer Josep Rovira i Virgili University, Spain
- Fernandes Stenio Federal University of Pernambuco (UFPE), Brazil
- Ghernaouti-Hélie Solange University of Lausanne, Switzerland
- Juan Miguel S2 Grupo, Spain
- Mahapatra Rabinarayan Texas A & M, USA
- Manzalini Antonio Telecom Italia, Italy
- McCusker Owen Sonalysts, USA
- Megias David Universitat Oberta de Catalunya, Spain
- Montero Javier Universidad Complutense de Madrid, Spain
- Moore Frank W University of Alaska Anchorage, USA
- Mukkamala, Srinivas New Mexico Tech, USA
- Ramaswamy Srini ABB Corporate Research Center, Bangalore, India
- Rehak Martin Czech Technical University, Czech Republic
- Sakurai Kouichi Kyushu University, Japan
- Suarez de Tangil Guillermo Universidad Carlos III de Madrid, Spain
- Sural Shamik Indian Institute of Technology, Kharagpur, India
- Tan Kay Chen National University of Singapore
- Torra Vicenç CSIC, Spain
- Upadhyaya Shambhu State University of New York at Buffalo, USA
- Villalón Antonio S2 Grupo, Spain
- Wang Xinyuan (Frank) George Mason University, USA
- Yao Xin University of Birmingham, UK
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.
