− A new trend in the design of fuzzy rulebased systems is the use of evolutionary multiobjective optimization (EMO) algorithms. This trend is observed in various areas in machine learning. EMO algorithms are often used to search for a number of Pareto-optimal non-linear systems with respect to their accuracy and complexity. In this paper, we first explain some basic concepts in multiobjective o...

h a stochastic and fuzzy environment, a multiobjective fuzzy stopping problem is discussed. The randomness and fuzziness are evaluated by probabilistic expectations and linear ranking functions, respectively. Pareto optimal fuzzy stopping times are given under the assumption of regularity for stopping rules, by using X-optimal stopping times. @ 2003 Elsevier Ltd. All rights reserved. &Fords-Mul...

A previously developed fuzzy waste load allocation model (FWLAM) for a river system is extended to address uncertainty involved in fixing the membership functions for the fuzzy goals of the pollution control agency (PCA) and the dischargers using the concept of grey systems. The model provides flexibility for the PCA and the dischargers to specify their goals independently, as the parameters fo...

The normative approach for decision-making is the dominant paradigm in designing intelligent decision agents. We discuss here the advantages of a more flexible way based on fuzzy logic. However, most exploitation methods of fuzzy preference relations do not provide good prescriptions. Recently some approaches based on the idea of reducing inconsistencies using evolutionary multiobjective optimi...

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative...

This paper considers a capacitated vehicle routing problem with fuzzy random travel time and demand (FRVRP). A chance-constrained multiobjective programming is presented based on fuzzy random theory and converted to a crisp equivalent models under some assumptions. To solve such a multiobjective combinatorial optimization problem, this paper presents a hybrid multiobjective particle swarm optim...

In this paper, we present an evolutionary multiobjective learning model achieving cooperation between the rule base and the adaptive fuzzy operators of the inference system in order to obtain simpler, more compact and still accurate linguistic fuzzy models by learning fuzzy inference adaptive operators together with rules. The multiobjective evolutionary algorithm proposed generates a set of fu...

Multiobjective mathematical programming problems, in particular the vector optimization problems, de ne a well known and studied area because of its relevance to numerous practical applications. In this paper vector optimization problems with a fuzzy nature are considered. In these problems usually it is assumed that all the objective functions involved come from the same decision maker. The pr...