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 incompl...
详细信息
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 codedgenetic Algorithm (RBLGA) and a binarycodedgenetic 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 codedgenetic 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 codedgenetic 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-alpha, 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. (C) 2004 Elsevier Ltd. All rights reserved.
In this study, parameter estimation in mathematical models using the real codedgeneticalgorithms (RCGA) approach is presented. Although the RCGA is similar with the binary coded genetic algorithms (BCGA) in terms of...
详细信息
In this study, parameter estimation in mathematical models using the real codedgeneticalgorithms (RCGA) approach is presented. Although the RCGA is similar with the binary coded genetic algorithms (BCGA) in terms of genetic process, it has few advantages such as high precision, non-existence of Hamming's cliff etc., over the BCGA. In this approach, creating initial population and selection procedure are almost the same with the BCGA, but crossover and mutation operations. The proposed approach is implemented on the second order ordinary differential equations modeling the enzyme effusion problem and it is compared with previous approaches. The results indicate that the proposed approach produced better estimated results with respect to previous findings. (C) 2008 Elsevier Ltd. All rights reserved.
暂无评论