majority of models in location literature are based on assumptions such as point demand, absence of competitors, as well as monopoly in location, products, and services. however in real-world applications, these assumptions are not well-matched with reality. in this study, a new mixed integer nonlinear programming model based on weighted goal programming approach is proposed to maximize the cap...

In this paper, we integrate goal programming (GP), Taylor Series, Kuhn-Tucker conditions and Penalty Function approaches to solve linear fractional bi-level programming (LFBLP)problems. As we know, the Taylor Series is having the property of transforming fractional functions to a polynomial. In the present article by Taylor Series we obtain polynomial objective functions which are equivalent to...

Apparent shear stress acting on a vertical interface between the main channel and floodplain in a compound channel serves to quantify the momentum transfer between sub sections of this cross section. In this study, three soft computing methods are used to simulate apparent shear stress in prismatic compound channels. The Genetic Algorithm Artificial neural network (GAA), Genetic Programming (GP...

This paper investigates the speed improvements available when using a graphics processing unit (GPU) for evaluation of individuals in a genetic programming (GP) environment. An existing GP system is modified to enable parallel evaluation of individuals on a GPU device. Several issues related to implementing GP on GPU are discussed, including how to perform tree-based GP on a device without recu...

Abstract. A parallel implementation of Genetic Programming (GP) is described, using the Bulk SynchronousParallel Programming (BSP) model, as implemented by the Oxford BSP library. Two approaches to the parallel implementation of GP are examined. The first is based on global parallelisation while the second implements the island model for evolutionary algorithms. It is shown that considerable sp...

In this paper, we show some experimental results of tree-adjunct grammar guided genetic programming [6] (TAG3P) on the symbolic regression problem, a benchmark problem in genetic programming. We compare the results with genetic programming [9] (GP) and grammar guided genetic programming [14] (GGGP). The results show that TAG3P significantly outperforms GP and GGGP on the target functions attemp...

A key concern in genetic programming GP is the size of the state space which must be searched for large and complex problem do mains One method to reduce the state space size is by using Strongly Typed Genetic Programming STGP We applied both GP and STGP to construct cooperation strate gies to be used by multiple predator agents to pursue and capture a prey agent on a grid world This domain has...