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Particle Swarm Optimization and Hill-Climbing Optimized Rough Sets for Modeling Interstate Conflict

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Militarized Conflict Modeling Using Computational Intelligence

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

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Abstract

This chapter presents methods to optimally granulize rough set partition sizes using particle swarm optimization and hill climbing techniques. These two methods are then compared to the equal-width-bin partitioning technique. The results obtained demonstrated that hill climbing provides higher forecasting accuracy, followed by the particle swarm optimization method, which was better than the equal-width-bin technique.

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Authors and Affiliations

  1. University of Johannesburg, Johannesburg, South Africa

    Tshilidzi Marwala & Dr. Monica Lagazio

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  1. Tshilidzi Marwala
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  2. Dr. Monica Lagazio
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Correspondence to Tshilidzi Marwala .

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Marwala, T., Lagazio, M. (2011). Particle Swarm Optimization and Hill-Climbing Optimized Rough Sets for Modeling Interstate Conflict. In: Militarized Conflict Modeling Using Computational Intelligence. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-0-85729-790-7_8

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  • DOI: https://doi.org/10.1007/978-0-85729-790-7_8

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