Abstract
This paper proposes rough genetic algorithms based on the notion of rough values. A rough value is defined using an upper and a lower bound. Rough values can be used to effectively represent a range or set of values. A gene in a rough genetic algorithm can be represented using a rough value. The paper describes how this generalization facilitates development of new genetic operators and evaluation measures. The use of rough genetic algorithms is demonstrated using a simple document retrieval application.
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© 1999 Springer-Verlag Berlin Heidelberg
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Lingras, P., Davies, C. (1999). Rough Genetic Algorithms. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_7
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DOI: https://doi.org/10.1007/978-3-540-48061-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66645-5
Online ISBN: 978-3-540-48061-7
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