Abstract
The first goal of this paper is to study the impact of Genetic Algorithms (GA’s) components such as encoding, different crossover and replacement strategies on the number and quality of extracted association rules. Moreover, we propose a strategy to manage the population. The later is organized in sub-populations where each one encloses same size rules. Each sub-population can be seen as a population on which a GA is applied. Hence, we propose two GAs, a sequential one and a parallel one. All tests are conducted on two types of benchmarks: synthetic and real ones of different sizes.
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- 1.
Acceleration = Sequential time/Parallel time.
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Hamdad, L., Benatchba, K., Bendjoudi, A., Ournani, Z. (2019). Impact of Genetic Algorithms Operators on Association Rules Extraction. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_62
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