• Industrial Engineering and Management Systems
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• v.7 no.3
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• pp.204-213
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• 2008

Companies that produce capital goods need to schedule the production of products that have complex product structures with components that require many operations on different machines. A feasible schedule must satisfy operation and assembly precedence constraints. It is also important to avoid deadlock situations. In this paper a Genetic Algorithm (GA) has been developed that includes a new repair process that rectifies infeasible schedules that are produced during the evolution process. The algorithm was designed to minimise the combination of earliness and tardiness penalties and took into account finite capacity constraints. Three different sized problems were obtained from a collaborating capital goods company. A design of experimental approach was used to systematically identify that the best genetic operators and GA parameters for each size of problem.

• Journal of the Korean Society of Industry Convergence
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• v.11 no.1
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• pp.27-34
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• 2008

This study presents techniques for the estimation of parameters in flood routing method of natural channel.. The Muskingum routing method is the most widely used method of hydrologic stream channel routing. In this paper, Genetic Algorithm and Fletcher-Powell method is applied to determine parameters(K and x) of the Muskingum routing method. The results of the approach shows that Genetic Algorithm method can be one of methods to determine parameters of the Muskingum routing method. Based on the analysis for estimated parameters and the comparison with the results from observed data, the applicability of Genetic Algorithm is verified.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.347-356
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• 2000

This paper compares four models of artificial neural networks (ANN) supported by genetic algorithms the prediction of stock price index. Previous research proposed many hybrid models of ANN and genetic algorithms(GA) in order to train the network, to select the feature subsets, and to optimize the network topologies. Most these studies, however, only used GA to improve a part of architectural factors of ANN. In this paper, GA simultaneously optimized multiple factors of ANN. Experimental results show that GA approach to simultaneous optimization for ANN (SOGANN3) outperforms the other approaches.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.252-256
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• 1997

The success of a fuzzy neural network(FNN) control system solving any given problem critically depends on the architecture of the network. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. This paper presents a new approach to structurally optimized designs of FNN models. A messy genetic algorithm is used to obtain structurally optimized FNN models. Structural optimization is regarded important before neural networks based learning is switched into. We have applied the method to the problem of a numerical approximation

• Journal of Communications and Networks
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• v.15 no.5
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• pp.486-495
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• 2013

In this article, we suggest a two-dimensional genetic algorithm (GA) method that applies a cognitive radio (CR) decision engine which determines the optimal transmission parameters for multicarrier communication systems. Because a CR is capable of sensing the previous environmental communication information, CR decision engine plays the role of optimizing the individual transmission parameters. In order to obtain the allowable transmission power of multicarrier based CR system demands interference analysis a priori, for the sake of efficient optimization, a two-dimensionalGA structure is proposed in this paper which enhances the computational complexity. Combined with the fitness objective evaluation standard, we focus on two multi-objective optimization methods: The conventional GA applied with the multi-objective fitness approach and the non-dominated sorting GA with Pareto-optimal sorting fronts. After comparing the convergence performance of these algorithms, the transmission power of each subcarrier is proposed as non-interference emission with its optimal values in multicarrier based CR system.

• Journal of applied mathematics & informatics
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• v.20 no.1_2
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• pp.327-343
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• 2006

A general methodology is suggested to solve shelf-space allocation problem of retailers. A multi-item inventory model of breakable items is developed, where items are either complementary or substitute. Demands of the items depend on the amount of stock on the showroom and unit price of the respective items. Also demand of one item decreases (increases) due to the presence of others in case of substitute (complementary) product. For such a model, a Contractive Mapping Genetic Algorithm (CMGA) has been developed and implemented to find the values of different decision variables. These are evaluated to have maximum possible profit out of the proposed system. The system has been illustrated numerically and results for some particular cases are derived. The results are compared with some other heuristic approaches- Simulated Annealing (SA), simple Genetic Algorithm (GA) and Greedy Search Approach (GSA) developed for the present model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.262-269
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• 2003

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Management (SCM). One of the key issues in the current SCM research area involved reducing both production and distribution costs. The purpose of this study is to determine the optimum quantity of production and transportation with minimum cost in the supply chain network. We have presented a mathematical model that deals with real world factors and constructs. Considering the complexity of solving such model, we have applied the genetic algorithm approach for solving this model computational experiments using a commercial genetic algorithm based optimizer. The results show that the real size problems we encountered can be solved In reasonable time

• Journal of Plant Biotechnology
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• v.5 no.4
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• pp.201-207
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• 2003

Tissue culture techniques arguably are an important approach for ex situ conservation of rare and endangered plant species. However, there is utmost importance on maintaining the genetic integrity of the introduced plants especially in tree species. To examine the genetic integrity of the micropropagated plants, we randomly screened few hardened plants of Syzygium travancoricum, a critically endangered tree taxon, using Randomly Amplified Polymorphic DNA (RAPD) markers. Twenty-three random. primers were tried and twenty-five polymorphic loci were identified. The dendrogram based on the Unweighted Pair-Group Method Arithmetic Average and Nei's similarity index depicted about 97% homology between the mother plants and micropropagated plants. Further, an attempt was made to reintroduce the micropropagated plants in the wild. Over three hundred small trees could be successfully established.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.133-136
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• 1997

This paper explores the optimal design methodology for auto lever using a genetic algorithm. Component of auto lever has been designed sequentially in the industry, but this study presents the novel design method to consider the design parameters of components simultaneously. The genetic algorithm approach is described to determine a set of design parameters for auto lever. The authors have attempted to model the design problem with the objective of minimizing the angle variation of detent spring subject to constraints such as modulus of elasticity of steel, geometry of shift pipe, and stiffness of spring. This method can give the better alternative.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.698-702
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• 1992

In this paper, Micro-Genetic algorithms(.mu.-GAs) is proposed on a minimum-time path planning for robot manipulator, which is a kind of optimization algorithm. The minimum-time path planning, which can allow the robot system to perform the demanded tasks with a minimum execution time, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computation burden and can't often find the optimal values. One way to overcome such difficulties is to apply the Micro-Genetic Algorithms, which can allow to find the optimal values, to the minimum-time problem. This paper propose an approach for solving the minimum-time path planning by using Micro-Genetic Algorithms. The effectiveness of the proposed method is demonstrated using the 2 d.o.f plannar Robot manipulator.