Abstract
ASHiCS (Automating the Search for Hazards in Complex Systems) uses evolutionary search on air traffic control simulations to find scenario configurations that generate high risk for a given air sector. Weighted heuristics are able to focus on specific events, flight paths or aircraft so that the search can effectively target incidents of interest. We describe how work on the characterization of our solution space suggests that destructive mutation operators perform badly in sensitive, high dimensional spaces. Finally, our work raises some issues about using collective risk assessment to discover significant safety events and whether the results are useful to safety analysts.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Koza, J., Keane, M., Streeter, M.: Evolving inventions. Scientific American, 52–59 (2003)
Fonlupt, C.: Book review: Genetic programming IV: Routine human competitive machine intelligence. Genetic Programming and Evolvable Machines 6, 231–233 (2005)
Alam, S., Zhao, W., Tang, J.: Discovering Delay Patterns in Arrival Traffic with Dynamic Continuous Descent Approaches using Co-Evolutionary Red Teaming. In : 9th ATM Seminar, Berlin (2011)
Alam, S., Lokan, C., Abbass, H.: What can make an airspace unsafe? characterizing collision risk using multi-objective optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1--8 (2012)
White, D.R., Poulding, S.: A rigorous evaluation of crossover and mutation in genetic programming. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 220–231. Springer, Heidelberg (2009)
Goldberg, D.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley (1989)
De Jong, K., Spears, W.: An analysis of the interacting roles of population size and crossover in genetic algorithms. In: Schwefel, H.-P., Männer, R. (eds.) PPSN I. LNCS, vol. 1540. Springer, Heidelberg (1991)
Lima, C., Goldberg, D., Sastry, K., Lobo, F.: Combining competent crossover and mutation operators: A probabilistic model building approach. In: Beyer, H.-G. (ed.) Proceedings of the 2005 Conference on Genetic and Evolutionary Computation (GECCO 2005), New York, pp. 735–742 (2005)
Perrin, E., Kirwan, B., Stroup, R.: A Systemic model of ATM Safety: the integrated risk picture. In: 7th ATM Seminar, Barcelona (2007)
ARMS Working Group, 2007-2010: The ARMS Methodology for Operational Risk Assessment in Aviation Organisations. (v 4.1, March 2010)
Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When Is Nearest Neighbor Meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)
Merz, P., Freisleben, B.: On the effectiveness of evolutionary search in high-dimensional NK-landscapes. In: IEEE World Congress on Computational Intelligence, Evolutionary Computation Proceedings, pp. 741–745 (1998)
Anderson, D., Lin, X.: A collision risk model for a crossing track separation methodology. Journal of Navigation 49(3), 337–349 (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Clegg, K., Alexander, R. (2014). Searching for Risk in Large Complex Spaces. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_61
Download citation
DOI: https://doi.org/10.1007/978-3-662-45523-4_61
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45522-7
Online ISBN: 978-3-662-45523-4
eBook Packages: Computer ScienceComputer Science (R0)