• Title/Summary/Keyword: Genetic Improvement

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Recent advance in genetic transformation of tall fescue (형질전환 톨 페스큐 개발의 최근 동향)

  • Lee, Ki-Won;Lee, Sang-Hoon;Kim, Kyung-Hee;Lee, Byung-Hyun
    • Journal of Plant Biotechnology
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    • v.36 no.4
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    • pp.336-343
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    • 2009
  • Tall fescue is an open-pollinated, perennial, cool season grass species widely used for forage and turf. Tremendous progress has been made in genetic transformation of tall fescue in the past decade. Methods for generating transgenic tall fescue plants have been developed based on biolistic transformation and Agrobacterium-mediated transformation. Potentially useful agronomic genes have been tested to environmental stress tolerance, herbicide tolerance and improve forage quality in tall fescue plants. We review progress in biotechnological improvement of tall fescue and discuss future molecular breeding of this species.

Improvement of Minimum MSE Performance in LMS-type Adaptive Equalizers Combined with Genetic Algorithm

  • Kim, Nam-Yong
    • Journal of electromagnetic engineering and science
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    • v.4 no.1
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    • pp.1-7
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    • 2004
  • In this paper the Individual tap - Least Mean Square(IT-LMS) algorithm is applied to the adaptive multipath channel equalization using hybrid-type Genetic Algorithm(GA) for achieving lower minimum Mean Squared Error(MSE). Owing to the global search performance of GA, LMS-type equalizers combined with it have shown preferable performance in both global and local search but those still have unsatisfying minimum MSE performance. In order to lower the minimum MSE we investigated excess MSE of IT-LMS algorithm and applied it to the hybrid GA equalizer. The high convergence rate and lower minimum MSE of the proposed system give us reason to expect that it will perform well in practical multi-path channel equalization systems.

A Study on Hybrid Approach for Improvement of Optimization Efficiency using a Genetic Algorithm and a Local Minimization Algorithm (최적화의 효율향상을 위한 유전해법과 직접탐색법의 혼용에 관한 연구)

  • Lee, Dong-Kon;Kim, S.Y.;Lee, C.U.
    • IE interfaces
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    • v.8 no.1
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    • pp.23-30
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    • 1995
  • Optimization in the engineering design is to select the best of many possible design alternatives in a complex design space. One major problem of local minimization algorithm is that they often result in local optima. In this paper, a hybrid method was developed by coupling the genetic algorithm and a traditional direct search method. The proposed method first finds a region for possible global optimum using the genetic algorithm and then searchs for a global optimum using the direct search method. To evaluate the performance of the hybrid method, it was applied to three test problems and a problem of designing corrugate bulkhead of a ship.

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An Application of Genetic Algorithm to increase Transfer Capacity using Series Capacitor (직렬콘덴서를 이용한 송전용량증대를 위한 유전알고리즘 응용)

  • You, Seok-Ku;Kim, Kyu-Ho;Lee, Kyung-Hoon
    • Proceedings of the KIEE Conference
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    • 1995.07b
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    • pp.485-487
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    • 1995
  • This paper proposes a GAs-applied method for power system planning using series capacitors in order to control the flow of power as desired and utilize the existing transmission facilities to its transfer capacity limits. The control strategy problem is formulated as optimization problem. Also, in employing genetic algorithms to solve the optimization problems, real variable-based genetic algorithm is presented to save the coding processing time and obtain more accurate value of the variable. An application to IEEE 57-bus test system proves that the proposed method is effective for improvement of power system transfer capacity.

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A U-shape Mixed Model Assembly Line Balancing Problem for Processing Time and Physical Workload Using the Genetic Algorithm (유전 알고리듬을 이용한 U형태 혼합모델에서의 작업시간과 육체적 작업부하를 고려한 최적 라인밸런싱)

  • Choi, Gyung-Hyun;Kim, Chan-Woo
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.28 no.3
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    • pp.98-108
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    • 2005
  • The assembly line balancing problem has been focused by many research works because the efficient management of the assembly line might influence not only the quality of the products but also the working conditions for the workers. This paper deals with U-shape mixed-model assembly line balancing and considers both the processing time and the physical workloads. We suggest the goal programming approach for this situation and to overcome some difficulties of finding optimal solution, we adopt the genetic algorithm that is one of the most promising solution techniques. We tested several test problems and present the results that indicate some improvement for the line balancing as well as the stable performance of the algorithm.

A Heterogeneous VRP to Minimize the Transportation Costs Using Genetic Algorithm (유전자 알고리듬을 이용한 운행비용 최소화 다용량 차량경로문제)

  • Ym, Mu-Kyun;Jeon, Geon-Wook
    • IE interfaces
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    • v.20 no.2
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    • pp.103-111
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    • 2007
  • A heterogeneous VRP which considers various capacities, fixed and variable costs was suggested in this study. The transportation cost for vehicle is composed of its fixed and variable costs incurred proportionately to the travel distance. The main objective is to minimize the total sum of transportation costs. A mathematical programming model was suggested for this purpose and it gives an optimal solution by using OPL-STUDIO (ILOG CPLEX). A genetic algorithm which considers improvement of an initial solution, new fitness function with weighted cost and distance rates, and flexible mutation rate for escaping local solution was also suggested. The suggested algorithm was compared with the results of a tabu search and sweeping method by Taillard and Lee, respectively. The suggested algorithm gives better solutions rather than existing algorithms.

Directed Evolution in Protein Functionality Improvement (단백질 기능 향상을 위한 방향적 진화)

  • 강환구;김학성
    • KSBB Journal
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    • v.16 no.2
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    • pp.107-114
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    • 2001
  • The dynamic evolution process has resulted in the myriad shapes, functions, and systems evident in every living organism. For centuries, people have been harnessing the power of evolution to produce new varieties of plants and animals, such as producing tomatoes from berries and Chihuahuas from wolves. Now scientists are using it to produce better molecules, ranging from drugs to industrial chemicals, and doing it in days or weeks rather than eons. The ingenious process, which creates genetic diversity and selects those with desired features in the laboratory, is called directed evolution or test tube evolution. In this paper, concepts of directed molecular evolution and some examples will be discussed.

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Somatic Cells Count and Its Genetic Association with Milk Yield in Dairy Cattle Raised under Thai Tropical Environmental Conditions

  • Jattawa, D.;Koonawootrittriron, S.;Elzo, M.A.;Suwanasopee, T.
    • Asian-Australasian Journal of Animal Sciences
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    • v.25 no.9
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    • pp.1216-1222
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    • 2012
  • Somatic cells count (SCC), milk yield (MY) and pedigree information of 2,791 first lactation cows that calved between 1990 and 2010 on 259 Thai farms were used to estimate genetic parameters and trends for SCC and its genetic association with MY. The SCC were log-transformed (lnSCC) to make them normally distributed. An average information-restricted maximum likelihood procedure was used to estimate variance components. A bivariate animal model that considered herd-yr-season, calving age, and regression additive genetic group as fixed effects, and animal and residual as random effects was used for genetic evaluation. Heritability estimates were 0.12 (SE = 0.19) for lnSCC, and 0.31 (SE = 0.06) for MY. The genetic correlation estimate between lnSCC and MY was 0.26 (SE = 0.59). Mean yearly estimated breeding values during the last 20 years increased for SCC (49.02 cells/ml/yr, SE = 26.81 cells/ml/yr; p = 0.08), but not for MY (0.37 kg/yr, SE = 0.87 kg/yr; p = 0.68). Sire average breeding values for SCC and MY were higher than those of cows and dams (p<0.01). Heritability estimates for lnSCC and MY and their low but positive genetic correlation suggested that selection for low SCC may be feasible in this population as it is in other populations of dairy cows. Thus, selection for high MY and low SCC should be encouraged in Thai dairy improvement programs to increase profitability by improving both cow health and milk yield.

A Genetic Linkage Map of Soybean with RFLP, RAPD, SSR and Morphological Markers

  • Kim, Hong-Sik;Lee, Suk-Ha;Lee, Yeong-Ho
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.45 no.2
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    • pp.123-127
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    • 2000
  • The objective of this study was to develop a linkage map of soybean under the genetic background of Korean soybean. A set of 89 F/sub 5/ lines was developed from a cross between 'Pureunkong', which was released for soy-bean sprout, and 'Jinpumkong 2', which had no beany taste in seed due to lack of lipoxygenase 1, 2, and 3. A linkage map was constructed for this population with a set of 113 genetic markers including 7 restriction fragment length polymorphism (RFLP) markers, 79 randomly amplified polymorphic DNA (RAPD) markers, 24 simple sequence repeat(SSR) markers, and 3 morphological markers. The map defined approximately 807.4 cM of the soybean genome comprising 25 linkage groups with 98 polymorphic markers. Fifteen markers remained unlinked. Seventeen linkage groups identified here could be assigned to the respective 13 linkage groups in the USDA soybean genetic map. RFLP and SSR markers segregated at only single genetic loci. Fourteen of the 25 linkage groups contained at least one SSR marker locus. Map positions of most of the SSR loci and their linkages with RFLP markers were consistent with previous reports of the USDA soybean linkage groups. For RAPD, banding patterns of 13 decamer primers showed independent segregations at two or more marker loci for each primer. Only the segregation at op Y07 locus was expressed with codominant manner among all RAPD loci. As the soybean genetic map in our study is more updated, molecular approaches of agronomically important genes would be useful to improve Korean soybean improvement.

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Performance Improvement of Queen-bee Genetic Algorithms through Multiple Queen-bee Evolution (다중 여왕벌 진화를 통한 여왕벌 유전자알고리즘의 성능향상)

  • Jung, Sung-Hoon
    • Journal of the Korea Society of Computer and Information
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    • v.17 no.4
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    • pp.129-137
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    • 2012
  • The queen-bee genetic algorithm that we made by mimicking of the reproduction of queen-bee has considerably improved the performances of genetic algorithm. However, since we used only one queen-bee in the queen-bee genetic algorithm, a problem that individuals of genetic algorithm were driven to one place where the queen-bee existed occurred. This made the performances of the queen-bee genetic algorithm degrade. In order to solve this problem, we introduce a multiple queen-bee evolution method by employing another queen-bee whose fitness is the most significantly increased than its parents as well as the original queen-bee that is the best individual in a generation. This multiple queen-bee evolution makes the probability of falling into local optimum areas decrease and allows the individuals to easily get out of the local optimum areas even if the individuals fall into a local optimum area. This results in increasing the performances of the genetic algorithm. Experimental results with four function optimization problems showed that the performances of the proposed method were better than those of the existing method in the most cases.