Abstract
Binary factor analysis (BFA) is a nonhierarchical binary data analysis, based on reduction of binary space dimension. It allows us to find hidden relationships in binary data, which can be used for efficient data compression, data mining, or intelligent data comparison for information retrieval. It seems that genetic algorithm (GA) may be used to find the solution. This paper describes two GA variants usable for BFA. The better one is described in detail, and results of some experiments are shown, comparing it with other known BFA methods. The experiments reveal that the new method based on revised genetic algorithm performs very well.
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Keprt, A., Snášel, V. (2005). Binary Factor Analysis with Genetic Algorithms. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_128
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DOI: https://doi.org/10.1007/3-540-32391-0_128
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