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Indicator space configuration for early warning of violent political conflicts by genetic algorithms

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  • Petya Ivanova
  • Todor Tagarev

Abstract

Recognition of preconflict situations has a powerful potential for early warning of violent political conflicts. This paper focuses on the design and application of artificial neural networks as classifiers of preconflict situations. Achieving a desired level of performance of the neural network relies on the appropriate construction of recognition space (selection of indicators) and the choice of network architecture. A fast and effective method for the design of reliable neural recognition systems is described. It is based on genetic algorithm techniques and optimizes both the configuration of input space and the network parameters. The implementation of the methodology provides for increased performance of the classifier in terms of accuracy, generalization capacity, computational and data requirements. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Petya Ivanova & Todor Tagarev, 2000. "Indicator space configuration for early warning of violent political conflicts by genetic algorithms," Annals of Operations Research, Springer, vol. 97(1), pages 287-311, December.
    • Handle: RePEc:spr:annopr:v:97:y:2000:i:1:p:287-311:10.1023/a:1018961232006
    DOI: 10.1023/A:1018961232006
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    Cited by:

    1. Marvin L. King & David R. Galbreath & Alexandra M. Newman & Amanda S. Hering, 2020. "Combining regression and mixed-integer programming to model counterinsurgency," Annals of Operations Research, Springer, vol. 292(1), pages 287-320, September.
    2. Manuel Castejón-Limas & Joaquín Ordieres-Meré & Ana González-Marcos & Víctor González-Castro, 2011. "Effort estimates through project complexity," Annals of Operations Research, Springer, vol. 186(1), pages 395-406, June.

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