• Title/Summary/Keyword: Genetic techniques

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Improvement of Convergence Properties for Genetic Algorithms (유전자 알고리즘에 대한 수렴특성의 개선)

  • Lee, Hong-Kyu
    • Journal of Advanced Navigation Technology
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    • v.12 no.5
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    • pp.412-419
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    • 2008
  • Genetic algorithms are efficient techniques for searching optimum solution but have the premature convergence problem getting stuck in the local optimum according to the evolutionary operator. In this paper we analyzed the reason for converging to the local optimum and proposed the method which able transit to the global optimum from the local optimum. In these methods we used the variable evolutionary operator with the average hamming distance, to maintain the genetic diversity of the population for getting out of the local optimum. The theoretical results are proved by the simulation experiments.

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Applications of Micro Genetic Algorithms to Engineering Design Optimization (마이크로 유전알고리듬의 최적설계 응용에 관한 연구)

  • Kim, Jong-Hun;Lee, Jong-Soo;Lee, Hyung-Joo;Koo, Bon-Heung
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.27 no.1
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    • pp.158-166
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    • 2003
  • The paper describes the development and application of advanced evolutionary computing techniques referred to as micro genetic algorithms ($\mu$GA) in the context of engineering design optimization. The basic concept behind $\mu$GA draws from the use of small size of population irrespective of the bit string length in the representation of design variable. Such strategies also demonstrate the faster convergence capability and more savings in computational resource requirements than simple genetic algorithms (SGA). The paper first explores ten-bar truss design problems to see the optimization performance between $\mu$GA and SGA. Subsequently, $\mu$GA is applied to a realistic engineering design problem in the injection molding process optimization.

A Study on the Optimization of Parameters for Muskingum Routing Method (Muskingum 홍수 추적방법의 매개변수 최적화에 관한 연구)

  • Cho, Hyeon-Kyeong
    • 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.

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Application of Genetic Algorithm for Loss Minimization in Distribution Systems (배전계통에서 손실 최소화를 위한 유전자 알고리즘의 적용)

  • Jeon, Young-Jae;Kim, Hoon;Lee, Seung-Youn;Son, Hag-Sig;Park, Soung-Ok;Kim, Jae-Chul
    • Proceedings of the KIEE Conference
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    • 2000.07a
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    • pp.156-158
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    • 2000
  • This paper presents a efficient algorithm for loss reduction of distribution system by automatic sectionalizing switch operation in distribution systems of radial type. To apply genetic algorithm to reconfiguration of distribution system, in this paper we propose the string type and efficient reconfiguration procedure. We also discuss the more elaborate search techniques of solution space as well as the simple genetic algorithm. The experimental results show that the proposed genetic algorithm have the ability to search a good solution.

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A Study on Optimal Design of Composite Materials using Neural Networks and Genetic Algorithms (신경회로망과 유전자 알고리즘을 이용한 복합재료의 최적설계에 관한 연구)

  • 김민철;주원식;장득열;조석수
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1997.04a
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    • pp.501-507
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    • 1997
  • Composite material has very excellent mechanical properties including tensile stress and specific strength. Especially impact loads may be expected in many of the engineering applications of it. The suitability of composite material for such applications is determined not only by the usual paramenters, but its impactor energy-absorbing properties. Composite material under impact load has poor mechanical behavior and so needs tailoring its structure. Genetic algorithms(GA) is probabilistic optimization technique by principle of natural genetics and natural selection and neural networks(NN) is useful for prediction operation on the basis of learned data. Therefore, This study presents optimization techniques on the basis of genetic algorithms and neural networks to minimum stiffness design of laminated composite material.

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Genetic Algorithm Application to Machine Learning

  • Han, Myung-mook;Lee, Yill-byung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.7
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    • pp.633-640
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    • 2001
  • In this paper we examine the machine learning issues raised by the domain of the Intrusion Detection Systems(IDS), which have difficulty successfully classifying intruders. There systems also require a significant amount of computational overhead making it difficult to create robust real-time IDS. Machine learning techniques can reduce the human effort required to build these systems and can improve their performance. Genetic algorithms are used to improve the performance of search problems, while data mining has been used for data analysis. Data Mining is the exploration and analysis of large quantities of data to discover meaningful patterns and rules. Among the tasks for data mining, we concentrate the classification task. Since classification is the basic element of human way of thinking, it is a well-studied problem in a wide variety of application. In this paper, we propose a classifier system based on genetic algorithm, and the proposed system is evaluated by applying it to IDS problem related to classification task in data mining. We report our experiments in using these method on KDD audit data.

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Studies on Genetic Stability of Micropropagated Plants and, Reintroduction in an Endemic and Endangered Taxon: Syzygium travancoricum Gamble (Myrtacae)

  • Ajith Anand
    • 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.

Optimal Design of Submarine Pressure Hull Structures Using Genetic Algorithm (유전 알고리즘을 적용한 잠수함 압력선체 최적 구조설계)

  • Cho, Yoon Sik;Paik, Jeom Kee
    • Journal of the Society of Naval Architects of Korea
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    • v.54 no.5
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    • pp.378-386
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    • 2017
  • In this paper, a method is presented for the optimal design of submarine pressure hull structures by taking advantage of genetic algorithm techniques. The objective functions and design constraints in the process of structural optimization are based on the ultimate limit states of hull structures. One of the benefits associated with the utilization of genetic algorithm is that the optimization process can be completed within short generations of design variables for the pressure hull structure model. Applied examples confirm that the proposed method is useful for the optimal design of submarine pressure hull structures. Details of the design procedure with applied examples are documented. The conclusions and insights obtained from the study are summarized.

Optimum Design of Torsional Shafting Using Real-Coded Genetic Algorithm (실수코딩 유전알고리즘을 이용한 비틀림 축계의 최적설계)

  • 최명수;문덕홍;설종구
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.39 no.4
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    • pp.284-290
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    • 2003
  • It is very important to minimize the weight of shaft from the viewpoint of economics and manufacture. For minimizing effectively the diameter of shaft in torsional shafting, authors developed computer program using the real-coded genetic algorithm which is one of optimizing techniques and based on real coding representation of genetic algorithm. In order to confirm the accuracy and effectiveness of the developed computer program, the computational results by the developed program were compared with those of conventional strength, stiffness and vibration designs for a generator shafting.

The role natural resource planr new function

  • Lee, Myung sun
    • Proceedings of the Plant Resources Society of Korea Conference
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    • 2001.11b
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    • pp.5-10
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    • 2001
  • It is necessary for governmental supporting polices to collect. management and maintain the genetic resource with respects of diversity of useful natural resource plant and domestic variety for plant industry. There are the main tasks of functional plant industry to develope the techniques of systemic management of genetic resources with sufficient life because of having the key of human's fate with life and death and being the valuable thing to be Investigation. collection and introduction of genetic resource with natural resource plants. Agriculture after WTO is putting an emphasis on the development of functional plants with highly competitiveness In world market under long term and organizing plants Genetic resource of plant in the official institution are maintaining and managing to be possible the regeneration and reproduction if necessary. and utilizing whoever need them.

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