This paper proposes a decision support system for tactical air combat environment using a combination of unsupervised learning for clustering the data and an ensemble of three well-known genetic programming techniques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic programming (LGP), Multi-Expression Programming (MEP) and Gene E...

Genetic programming (GP) is one of the computer algorithms in the family of evolutionary-computational methods, which have been shown to provide reliable solutions to complex optimization problems. The genetic programming under discussion in this work relies on tree-like building blocks, and thus supports process modeling with varying structure. In this paper the systems containing amino ac...

bilevel linear programming  is a decision making problem with a two-level decentralized organization. the textquotedblleft leadertextquotedblright~ is in the upper level and the textquotedblleft followertextquotedblright, in the lower. making a decision at one level affects that at the other one. in this paper, bilevel linear programming  with inexact parameters has been studied and a method is...

In this paper, we present a model to measure attainment value of fuzzy stochastic goals. Then, the new measure is used to de-randomize and de-fuzzify the fuzzy stochastic goal programming problem and obtain a standard linear program (LP). A numerical example is provided to illustrate the proposed method.

Gene expression programming (GEP) is, like genetic algorithms (GAs) and genetic programming (GP), a genetic algorithm as it uses populations of individuals, selects them according to fitness, and introduces genetic variation using one or more genetic operators [1]. The fundamental difference between the three algorithms resides in the nature of the individuals: in GAs the individuals are linear...

This paper presents a fuzzy goal programming (FGP) methodology for solving bi-level quadratic programming (BLQP) problems. In the FGP model formulation, firstly the objectives are transformed into fuzzy goals (membership functions) by means of assigning an aspiration level to each of them, and suitable membership function is defined for each objectives, and also the membership functions for vec...

multiple objective programming (mop) problems have become famous among many researchers due to more practical and realistic implementations. there have been a lot of methods proposed especially during the past four decades. in this paper, we develop a new algorithm based on a new approach to solve mop problems by starting from a utopian point (which is usually infeasible) and moving towards the...

Realization of autonomous behavior in mobile robots, using fuzzy logic control, requires formulation of rules which are collectively responsible for necessary levels of intelligence. Such a collection of rules can be conveniently decomposed and eeciently implemented as a hierarchy of fuzzy-behaviors. This article describes how this can be done using a behavior-based architecture. A behavior hie...

This paper presents a system for accessing the potential of Genetic Programming [Koza 1992] by exploiting the availability of computer networks to distribute the computational load over a large number of machines. The goal is to utilise the CPU and memory resources of Intranet systems, to facilitate GP operations. In addition the use of a large-scale parallel network of machines allows a demeti...

This work deals with the resolution of the goal programming problem with linear fractional criteria. The main difficulty of these problems is the non-linear constraints of the mathematical programming models that have to be solved. When there exist solutions satisfying all target values, the problem is easy to solve by solving a linear problem. So, in this paper we deal with those instances whe...