Optimization of CBR Model Utilizing Genetic Algorithm

Abstract:

in the field of artificial intelligence, Case Based Reasoning (CBR) is an arising reasoning technique, in which choosing reasoning index has been the hot topic and difficulty. In order to get optimal feature subset in the process of index selection, this paper combined gray correlation analysis with genetic algorithm (GA) to optimize the feature selection process, taking the gray correlation analysis result as the initial population for GA heuristic search, which can on one hand get better feature combination, on the other hand, effectively reduce the evolution of GA, finally improving GAs execution efficiency. Based on this, an optimized GA-CBR case reasoning model is put forward, which has been proved by empirical results to have an improved CBR forecasting accuracy.

• Periodical: Applied Mechanics and Materials (Volumes 543-547)
• Pages: 1827-1831
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.543-547.1827
• Online since: March 2014
• Authors: Qi Shen*
• Keywords: Case-Based Reasoning (CBR), Feature Selection, Genetic Algorithm (GA), Gray Correlation Analysis, Optimization

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