This paper presents a proposal based on an evolutionary algorithm for imputing missing observations in time series. A genetic algorithm based on the minimization of an error function derived from their autocorrelation function, mean, and variance is presented. All methodological aspects of the genetic structure are presented. An extended description of the design of the fitness function is provided. Four application examples are provided and solved by using the proposed method.

No abstract

No abstract

No abstract

This paper shows an alternative methodology to find optimal solutions of a Linear Programming problem defined in a fuzzy environment. The classical Fuzzy Linear Programming (FLP) problem is treated by using fuzzy restrictions in the form Ax b where˘indicates a Type-1 Fuzzy Set (T1 FS). The proposed approach uses jointȂ andb fuzzy parameters to solve a linear programming model under uncertainty conditions. Triangular fuzzy sets are used to reduce the computational complexity of the model, however other types of fuzzy sets can be used. A Cumulative Membership Function (CMF) approach is defined, some optimality conditions are discussed and a new theorem is proved. Finally a small example is provided.