| Autor: |
Hanwen He, Xingchuan Lan, Chang Liu, Daoqu Geng |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). |
| DOI: |
10.1109/icbaie52039.2021.9390026 |
| Popis: |
The RETE algorithm greatly improves the efficiency of the production reasoning system by sharing rule conditions and saving temporary matching results, making it one of the most efficient production reasoning algorithms. However, with the increasing size of data, frequent changes of business information, and the emergence of incomplete data and fuzzy logic, the reasoning time and the storage consumption of the RETE algorithm in the reasoning process are increasing. Many studies are devoted to improving the RETE algorithm to reduce the inference time and the occupation of storage resources. In this article, we propose a method that using the genetic algorithm (GA) to optimize the Alpha network of the RETE algorithm. Experimental results show that genetic algorithm has an optimization effect on the rule matching system in reasoning time and memory consumption. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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