Real/binary-like coded versus binary coded genetic algorithms to automatically generate fuzzy knowledge bases: a comparative study

Nowadays fuzzy logic is increasingly used in decision-aided systems since it offers several advantages over other traditional decision-making techniques. The fuzzy decision support systems can easily deal with incomplete and/or imprecise knowledge applied to either linear or nonlinear problems. This paper presents the implementation of a combination of a Real/Binary-Like coded Genetic Algorithm (RBLGA) and a Binary coded Genetic Algorithm (BGA) to automatically generate Fuzzy Knowledge Bases (FKB) from a set of numerical data.

Both algorithms allow one to fulfill a contradictory paradigm in terms of FKB precision and simplicity (high precision generally translates into a higher level of complexity) considering a randomly generated population of potential FKBs. The RBLGA is divided into two principal coding methods: (1) a real coded genetic algorithm that maps the fuzzy sets repartition and number (which drives the number of fuzzy rules) into a set of real numbers and (2) a binary like coded genetic algorithm that deals with the fuzzy rule base relationships (a set of integers).

The BGA deals with the entire FKB using a single bit string, which is called a genotype. The RBLGA uses three reproduction mechanisms, a BLX-α, a simple crossover and a fuzzy set reducer, while the BGA uses a simple crossover, a fuzzy set displacement mechanism and a rule reducer. Both GAs are tested on theoretical surfaces, a comparison study of the performances is discussed, along with the influences of some evolution criteria.