Selecting fuzzy if-then rules for classification problems using genetic algorithms

Selecting fuzzy if-then rules for classification problems using genetic algorithms

This paper proposes a genetic-algorithm-based method for selecting a small number of significant fuzzy if-then rules to construct a compact fuzzy classification system with high classification power. The rule selection problem is formulated as a combinatorial optimization problem with two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy if-...

Optimization Under Fuzzy If-Then Rules Using Stochastic Algorithms

A new approach to optimization of processes described by fuzzy rules, in which the functional relationship between the decision variables and the objective function is not completely known, is introduced in this paper. It is based on stochastic algorithms and allows to determine optimal values of state variables and to optimize fuzzy rules (parameters of membership functions). Stochastic algori...

Fuzzy if-then rules for modeling interdependences in FMOP problems

There has been a growing interest and activity in the area of multiple criteria decision making (MCDM), especially in the last 25 years. modeling and optimization methods have been developed in both crisp and fuzzy environments. The overwhelming majority of approaches for finding best compromise solutions to MCDM problems do not make use of the interdependences among the objectives. However, as...

Optimization under fuzzy if-then rules

The aim of this paper is to introduce a novel statement of fuzzy mathematical programming problems and to provide a method for findig a fair solution to these problems. Suppose we are given a mathematical programming problem in which the functional relationship between the decision variables and the objective function is not completely known. Our knowledge-base consists of a block of fuzzy if-t...

RBF Networks Versus Fuzzy If - Then Rules