Abstract
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-a; 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.
Original language | English |
---|---|
Journal | Engineering Applications of Artificial Intelligence |
Volume | 17 |
Issue number | 4 |
Pages (from-to) | 313-325 |
ISSN | 0952-1976 |
DOIs | |
Publication status | Published - 2004 |
Externally published | Yes |