• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.297-300
• /
• 1997

This paper uses genetic algorithm (GA) for optimal control. GA can find optimal control profile, but the profile may be oscillating feature. To make profile smooth, fuzzy genetic algorithm (FGA) is proposed. GA with fuzzy logic techniques for optimal control can make optimal control profile smooth. We describe the Fuzzy Genetic Algorithm that uses a fuzzy knowledge based system to control GA search. Result from the simulation example shows that GA can find optimal control profile and FGA makes a performance improvement over a simple GA.

• Journal of Korea Multimedia Society
• /
• v.4 no.1
• /
• pp.82-90
• /
• 2001

Genetic Algorithm(GA) has known as a method of solving NP problem in various applications. Since a major drawback of the GA is that it needs a long computation time, the hardware implementation of Genetic Algorithm is focused on in recent studies. This paper proposes a new type of embedded one chip computer fort Hardware Implementation of Genetic Algorithm. The proposed embedded one chip computer consists of 16 Bit CPU care and hardware of genetic algorithm. In contrast to conventional hardware oriented GA which is dependent on main computer in the process of GA, the proposed embedded one chip computer is independent on main computer. Conventional hardware GA uses the fixed length of chromosome but the proposed embedded one chip computer uses the variable length of chromosome by employing the efficient 16 bit Pipeline Unit. Experimental results show that the proposed one chip computer is applicable to the design of evolvable hardware for Random NRZ bit synchronization circuit.

• Journal of Korean Association for Spatial Structures
• /
• v.8 no.3
• /
• pp.75-82
• /
• 2008

In this study, the modified genetic algorithm, D-GA, is proposed. D-GA is a hybrid genetic algorithm combined a simple genetic algorithm and the local search algorithm using direction vectors. Also, two types of direction vectors, learning direction vector and random direction vector, are defined without the sensitivity analysis. The accuracy of D-GA is compared with that of simple genetic algorithm. It is demonstrated that the proposed approach can be an effective optimization technique through a minimum weight structural optimization of ten bar truss.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.21 no.1
• /
• pp.147-161
• /
• 1996

Generally the Multiprocessor Scheduling (MPS) problem is difficult to solve because of the precedence of the tasks, and it takes a lot of time to obtain its optimal solution. Though Genetic Algorithm (GA) does not guarantee the optimal solution, it is practical and effective to solve the MPS problem in a reasonable time. The algorithm developed in this research consists of a improved GA and GP/MISF (Critical Path/Most Immediate Successors First). An efficient genetic operator is derived to make GA more efficient. It runs parallel CP/MISF with GA to complement the faults of GA. The solution by the developed algorithm is compared with that of CP/MISF, and the better is taken as a final solution. As a result of comparative analysis by using numerical examples, although this algorithm does not guarantee the optimal solution, it can obtain an approximate solution that is much closer to the optimal solution than the existing GA's.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1995.09a
• /
• pp.220-229
• /
• 1995

Generally the Multiprocessor Scheduling(MPS) problem is difficult to solve because of the precedence of the tasks, and it takes a lot of time to obtain its optimal solution. Though Genetic Algorithm(GA) does not guarantee the optimal solution, it is practical and effective to solve the MPS problem in a reasonable time. The algorithm developed in this research consists of a improved GA and CP/MISF(Critical Path/Most Immediate Successors First). A new genetic operator is derived to make GA more efficient. It runs parallel CP/MISF with Ga to complement the faults of GA. The solution by the developed algorithm is compared with that of CP/MISF, and the better is taken as a final solution. As a result of comparative analysis by using numerical examples, although this algorithm does not guarantee the optimal solution, it can obtain an approximate solution that is much closer to the optimal solution than the existing GA's.

• ETRI Journal
• /
• v.34 no.5
• /
• pp.696-705
• /
• 2012

Accurate synthesis models based on artificial neural networks (ANNs) are proposed to directly obtain the physical dimensions of an asymmetric coplanar waveguide with conductor backing and substrate overlaying (ACPWCBSO). First, the ACPWCBSO is analyzed with the conformal mapping technique (CMT) to obtain the training data. Then, a modified genetic-algorithm-Levenberg-Marquardt (GA-LM) algorithm is adopted to train ANNs. In the algorithm, the maximal relative error (MRE) is used as the fitness function of the chromosomes to guarantee that the MRE is small, while the mean square error is used as the error function in LM training to ensure that the average relative error is small. The MRE of ANNs trained with the modified GA-LM algorithm is less than 8.1%, which is smaller than those trained with the existing GA-LM algorithm and the LM algorithm (greater than 15%). Lastly, the ANN synthesis models are validated by the CMT analysis, electromagnetic simulation, and measurements.

• Journal of Korea Society of Industrial Information Systems
• /
• v.24 no.5
• /
• pp.53-64
• /
• 2019

The term 're-use' here signifies the re-use of end-of-life products without changing their form after they have been thoroughly inspected and cleaned. In the re-use network model, the distributor determines the product order quantity on the network through which new products are received from the suppliers or products are supplied to the customers through re-use of the recovered products. In this paper, we propose a reusable network model for reusable products that considers the total logistics cost from the forward logistics to the reverse logistics. We also propose a reusable network model that considers the processing and disposal costs for reuse in an optimal inventory management environment. The authors employe Genetic Algorithm (GA), which is one of the optimization techniques, to verify the validity of the proposed model. And in order to investigate the effect of the parameters on the solution, the priority-based GA (priGA) under three different parameters and the modified Hybrid GA (mhGA), in which parameters are adjusted for each generation, were applied to four examples with varying sizes in the simulation.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.12 no.3
• /
• pp.82-88
• /
• 2003

SGA(Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, ${\mu}GA$(Micro-Genetic Algorithm) has recently been developed. In this study, ${\mu}GA$ which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of ${\mu}GA$ were compared with those of SGA. Solutions of ${\mu}GA$ for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that ${\mu}GA$ is a suitable and very efficient optimization algorithm for structural design.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2003.04a
• /
• pp.9-14
• /
• 2003

SGA (Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, $\mu$GA(Micro-Genetic Algorithm) has recently been developed. In this study, $\mu$GA which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of $\mu$GA were compared with those of SGA. Solutions of $\mu$GA for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that $\mu$GA is a suitable and very efficient optimization algorithm for structural design.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.24 no.10
• /
• pp.149-156
• /
• 2010

In this paper, the optimal design of a wind generator, implemented with the hybridized GA(Genetic Algorithm) and ES(Expert System), has been performed to maximize the AEP(Annual Energy Production) over the whole wind speed characterized by the statistical model of wind speed distribution. In particular, to solve the problem of calculation iterate, ES finds the superior individual and apply to initial generation of GA and it makes reduction of search domain. Meanwhile, for effective searching in reduced search domain, it propose Intelligent GA algorithm. Also, it shows the results of optimized model 500[kW] wind generator using hybridized algorithm and benchmark result of compare with GA.