• DOI:10.1007/978-3-642-15234-4_25
• Corpus ID: 639992

Evolutionary-Computation Based Risk Assessment of Aircraft Landing Sequencing Algorithms

@inproceedings{Zhao2010EvolutionaryComputationBR,
  title={Evolutionary-Computation Based Risk Assessment of Aircraft Landing Sequencing Algorithms},
  author={Wenjing Zhao and Jiangjun Tang and Sameer Alam and Axel Bender and Hussein A. Abbass},
  booktitle={DIPES/BICC},
  year={2010}
}

• Wenjing Zhao, Jiangjun Tang, +2 authors H. Abbass
• Published in DIPES/BICC 20 September 2010
• Computer Science

Usually, Evolutionary Computation (EC) is used for optimisation and machine learning tasks. Recently, a novel use of EC has been proposed – Multiobjective Evolutionary Based Risk Assessment (MEBRA). MEBRA characterises the problem space associated with good and inferior performance of computational algorithms. Problem instances are represented (“scenario Representation”) and evolved (“scenario Generation”) in order to evaluate algorithms (“scenario Evaluation”). The objective functions aim at maximising or minimising the success rate of an algorithm. In the “scenario Mining” step, MEBRA identifies the patterns common in problem instances when an algorithm performs best in order to understand when to use it, and in instances when it performs worst in order to understand when not to use it.