in this paper, genetic algorithms (gas) are employed to control simultaneous linear systems in both state and output feedback. first, the similarity transformation is applied to obtain parameterized controllers. this requires solution of a system of equations and also some non-linear inequalities. gas are used to solve these equations and inequalities. therefore, the paper presents an analytica...

optimization is an important methodology for activities in planning and design. the transportation designers are able to introduce better projects when they can save time and cost of travel for project by optimization methods. most of the optimization problems in engineering are more complicated than they can be solved by custom optimization methods. the most common and available methods are he...

This paper is an attempt to make the discussion of parallel genetic algorithms independent from hardware issues. There have been many parallel implementations of genetic algorithms, some of them on hardware that is not even available any more. Most of these implementations have also modified the structure of the genetic algorithm for matters of efficiency, and it has been reported that these mo...

This paper outlines the development of a new evolutionary algorithms based timetabling (EAT) tool for solving course scheduling problems that include a genetic algorithm (GA) and a memetic algorithm (MA). Reproduction processes may generate infeasible solutions. Previous research has used repair processes that have been applied after a population of chromosomes has been generated. This research...

The main purpose of this paper is to solve an inverse random differential equation problem using evolutionary algorithms. Particle Swarm Algorithm and Genetic Algorithm are two algorithms that are used in this paper. In this paper, we solve the inverse problem by solving the inverse random differential equation using Crank-Nicholson's method. Then, using the particle swarm optimization algorith...

The problem of multiprocessor scheduling can be stated as scheduling a general task graph on a multiprocessor system such that a set of performance criteria will be optimized. This study investigates the use of near optimal scheduling strategies in multiprocessor scheduling problem. The multiprocessor scheduling problem is modeled and simulated using five different simulated annealing algorithm...

This work addresses the problem of finding the adjustable parameters of a learning algorithm using Genetic Algorithms. This problem is also known as the model selection problem. In this paper, some model selection techniques (e.g., crossvalidation and bootstrap) are used as objective functions of a Genetic Algorithm. The Genetic Algorithm is modified in order to allow the efficient use of these...

This paper considers the job scheduling problem in virtual manufacturing cells (VMCs) with the goal of minimizing two objectives namely, makespan and total travelling distance. To solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (NSGA-II) and knowledge-based non-dominated sorting genetic algorithm (KBNSGA-II). The difference between these algor...

scheduling of production in flexible manufacturing systems (fmss) has been extensively investigated over the past years and it continues to attract the interest of both academic researchers and practitioners. the generation of new and modified production schedules is becoming a necessity in today’s complex manufacturing environment. genetic algorithms are used in this paper to obtain an initial...