This study presents a new approach to solve multi-response simulation optimization problems. This approach integrates a simulation model with a genetic algorithm heuristic and a goal programming model. The genetic algorithm technique offers a very flexible and reliable tool able to search for a solution within a global context. This method was modified to perform the search considering the mean...

in this study, we present a mrcpsp/max (multi-mode resource-constrained project scheduling problem with minimum and maximum time lags) model with minimization tardiness costs and maximization earliness rewards of activities as objective. the proposed model is nearby to real-world problems and has wide applications in various projects. this problem is not available in the literature exactly and ...

The following MOEA algorithms are briefly summarized and compared: • NPGA Niched Pareto Genetic Algorithm (1994) – NPGA II (2001) • NSGA Non-dominated Sorting Genetic Algorithm (1994) – NSGA II (2000) • SPEA Strength Pareto Evolutionary Algorithm (1998) – SPEA2 (2001) – SPEA2+ (2004) – ISPEA Immunity SPEA (2003) • PAES Pareto Archived Evolution Strategy (2000) – M-PAES Mimetic PAES (2000) • PES...

Bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. In this study, measured bedload by Helley- Smith sampler was used to estimate the bedload transport of Kurau River in Malaysia. An artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estim...

This paper describes the application of multitype, self-adaptive genetic programming techniques, implemented in the PushGP and Pushpop systems, to the automatic programming of multi-agent systems. It includes a brief case study of the application of PushGP to a transport network control problem and a demonstration of self-adaptive modularization in a dynamic environment that was developed by Va...

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 acids ...

The use of intelligent systems for stock market predictions has been widely established. This chapter introduces two Genetic Programming (GP) techniques: Multi-Expression Programming (MEP) and Linear Genetic Programming (LGP) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm and Takagi-Sugeno n...

in this paper we try to introduce a new approach and study the notion of efficiency under a multi objectives linear programming problem in the university by using analysis of hierarchy process (ahp)‎. ‎to this end‎, ‎we first extract some effective parameters due to efficiency offices in university and then prioritized these parameters by the ahp method‎. ‎hence‎, ‎we could classify the most im...