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.
|Title of host publication||IEEE Fourth International Conference on Control and Automation|
|Publication status||Published - 2003|
|Event||IEEE Fourth International Conference on Control and Automation - Montreal, Quebec, Canada|
Duration: 1 Jan 2003 → …
|Conference||IEEE Fourth International Conference on Control and Automation|
|City||Montreal, Quebec, Canada|
|Period||01/01/2003 → …|