Abstract
This paper presents the results of the implementation of a combination of a real-coded and binary-like coded genetic algorithm (RBLGA) to automatically generate fuzzy knowledge bases (FKB) from a set of numerical data. The algorithm allows one to fulfil a contradictory paradigm in term of FKB precision and simplicity (high precision generally translates into high complexity level) considering a randomly generated population of potential FKBs. The RBLGA is divided in two principal coding ways, on one hand a real coded genetic algorithm (RCGA) that maps the fuzzy sets repartition and number (which drives the number of fuzzy rules) into a set of real numbers, on the other hand a binary like genetic algorithm deals with the fuzzy rule base (a set of integer numbers is used). The RBLGA uses three reproduction mechanisms, a BLX-alpha, a simple crossover and a fuzzy set reducer. The RBLGA is validated through a theoretical surface and, finally, applied to a set of experimental data.
| Original language | English |
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| Title of host publication | IEEE Fourth International Conference on Control and Automation |
| Publication date | 2003 |
| Pages | 799-803 |
| Publication status | Published - 2003 |
| Externally published | Yes |
| Event | 2003 4th International Conference on Control and Automation - Montreal, Canada Duration: 12 Jun 2003 → … Conference number: 4 https://ieeexplore.ieee.org/xpl/conhome/10630/proceeding |
Conference
| Conference | 2003 4th International Conference on Control and Automation |
|---|---|
| Number | 4 |
| Country/Territory | Canada |
| City | Montreal |
| Period | 12/06/2003 → … |
| Internet address |