This paper presents a method to solve a linear or nonlinear Fuzzy Multiobjective Programming problem. We propose Genetic Search based approaches to the case in which fuzzy constraints are assumed.

In this paper, two kinds of fuzzy approaches are proposed for not only multiobjective stochastic linear programming problems, but also multiobjective fuzzy random linear programming problems through a probability maximization model. In a probability maximization model, it is necessary for the decision maker to specify permissible values of objective functions in advance, which have a great infl...

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...

In this paper, two kinds of fuzzy approaches are proposed for not only multiobjective stochastic linear programming problems, but also multiobjective fuzzy random linear programming problems through a probability maximization model. In a probability maximization model, it is necessary for the decision maker to specify permissible values of objective functions in advance, which have a great infl...

In this paper, we demonstrate that simple fuzzy rule-based classification systems with high interpretability are obtained through multiobjective genetic rule selection. In our approach, first a prespecified number of candidate fuzzy rules are extracted from numerical data in a heuristic manner using rule evaluation criteria. Then multiobjective genetic rule selection is applied to the extracted...

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...

Robust design intends to optimize both the expected value and the deviation of the performance function and can be obtained by minimizing performance variab ility. Fuzzy multiobjective optimizat ion describes the performance criteria, called goals, and the constraints in terms of fuzzy membership functions. The membership function of the optimum value the decision – is obtained as conjunction o...

This paper proposes a control system design method by the multiobjective fuzzy satisficing approach using Genetic Algorithm (GA). First, a control system design is formulated as a constrained multiobjective optimization problem, and it is transformed into a fuzzy satisficing problem by introducing the aspiration level and the unsatisfying function. It is solved by a GA interactively. Then, the ...

In this paper, we review the development of studies on multiobjective noncooperative games, and particularly we focus on nondominated equilibrium solutions in multiobjective two-person nonzero-sum games in normal and extensive forms. After outlining studies related to multiobjective noncooperative games, we treat multiobjective two-person nonzero-sum games in normal form, and a mathematical pro...

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...