• Title, Summary, Keyword: Genetic Algorithm

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A Study to Improve the Return of Stock Investment Using Genetic Algorithm (유전자 알고리즘을 이용한 주식투자 수익률 향상에 관한 연구)

  • Cho He Youn;Kim Young Min
    • The Journal of Information Systems
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    • v.12 no.2
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    • pp.1-20
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    • 2003
  • This paper deals with the application of the genetic algorithm to the technical trading rule of the stock market. MACD(Moving Average Convergence & Divergence) and the Stochastic techniques are widely used technical trading rules in the financial markets. But, it is necessary to determine the parameters of these trading rules in order to use the trading rules. We use the genetic algorithm to obtain the appropriate values of the parameters. We use the daily KOSPI data of eight years during January 1995 and October 2002 as the experimental data. We divide the total experimental period into learning period and testing period. The genetic algorithm determines the values of parameters for the trading rules during the teaming period and we test the performance of the algorithm during the testing period with the determined parameters. Also, we compare the return of the genetic algorithm with the returns of buy-hold strategy and risk-free asset. From the experiment, we can see that the genetic algorithm outperforms the other strategies. Thus, we can conclude that genetic algorithm can be used successfully to the technical trading rule.

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A service Restoration and Optimal Reconfiguration of Distribution Network Using Genetic Algorithm and Tabu Search (유전 알고리즘과 Tabu Search를 이용한 배전계통 사고복구 및 최적 재구성)

  • Cho, Chul-Hee;Shin, Dong-Joon;Kim, Jin-O
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.50 no.2
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    • pp.76-82
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    • 2001
  • This paper presents a approach for a service restoration and optimal reconfiguration of distribution network using Genetic algorithm(GA) and Tabu search(TS) method. Restoration and reconfiguration problems in distribution network are difficult to solve in short times, because distribution network supplies power for customers combined with many tie-line switches and sectionalizing switches. Furthermore, the solutions of these problems have to satisfy radial operation conditions and reliability indices. To overcome these time consuming and sub-optimal problem characteristics, this paper applied Genetic-Tabu algorithm. The Genetic-Tabu algorithm is a Tabu search combined with Genetic algorithm to complement the weak points of each algorithm. The case studies with 7 bus distribution network showed that not the loss reduction but also the reliability cost should be considered to achieve the economic service restoration and reconfiguration in the distribution network. The results of suggested Genetic-Tabu algorithm and simple Genetic algorithm are compared in the case study also.

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Optimal Design of Squeeze Film Damper Using an Enhanced Genetic Algorithm (향상된 유전알고리듬을 이용한 스퀴즈 필름 댐퍼의 최적설계)

  • 김영찬;안영공;양보석
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • pp.805-809
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    • 2001
  • This paper is presented to determine the optimal parameters of squeeze film damper using an enhanced genetic algorithm (EGA). The damper design parameters are the radius, length and radial clearance of the damper. The objective function is minimization of a transmitted load between bearing and foundation at the operating and critical speeds of a flexible rotor. The present algorithm was the synthesis of a genetic algorithm with simplex method for a local concentrate search. This hybrid algorithm is not only faster than the standard genetic algorithm, but also gives a more accurate solution and can find both the global and local optimum solution. The numerical example is presented that illustrated the effectiveness of enhanced genetic algorithm for the optimal design of the squeeze film damper for reducing transmitted load.

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A Study on Optimal Design of Rocker Arm Shaft using Genetic Algorithm (유전자 알고리즘을 이용한 로커암 축의 최적설계에 관한 연구)

  • 안용수;이수진;이동우;홍순혁;조석수;주원식
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.198-202
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    • 2004
  • This study proposes a new optimization algorithm which is combined with genetic algorithm and ANOM. This improved genetic algorithm is not only faster than the simple genetic algorithm, but also gives a more accurate solution. The optimizing ability and convergence rate of a new optimization algorithm is identified by using a test function which have several local optimum and an optimum design of rocker arm shaft. The calculation results are compared with the simple genetic algorithm.

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An Intelligent Control of Mobile Robot Using Genetic Algorithm (유전자 알고리즘을 이용한 이동로봇의 지능제어)

  • 한성현
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.13 no.3
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    • pp.126-132
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    • 2004
  • This paper proposed trajectory tracking control based on genetic algorithm. Trajectory tracking control scheme are real coding genetic algorithm(RCGA) and back-propagation algorithm(BPA). Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studies have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using real coding genetic algorithm and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verity numerical simulations and the results show better performance than constant gain controller.

A Study on Optimal Design of Rocker Arm Shaft Using Improved Genetic Algorithm (개선된 유전자 알고리즘을 이용한 로커암 축의 최적설계에 관한 연구)

  • Lee Soo Jin;An Yong Su;Lee Dong Woo;Cho Seok Swoo;Joo Won Sik
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.29 no.6
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    • pp.835-841
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    • 2005
  • This study proposes a new optimization algorithm which is combined with genetic algorithm and ANOM. This improved genetic algorithm is not only faster than the simple genetic algorithm, but also gives a more accurate solution. The optimizing ability and convergence rate of a new optimization algorithm is identified by using a evaluation function which have several local optimum and an optimum design of rocker arm shaft. The calculation results are compared with the simple genetic algorithm.

Application of an Optimization Method to Groundwater Contamination Problems

  • Ko, Nak-Youl;Lee, Jin-Yong;Lee, Kang-Kun
    • Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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    • pp.24-27
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    • 2002
  • The optimal designs of groundwater problems of contaminant containment and cleanup using linear programming and genetic algorithm are provided. In the containment problem, genetic algorithm shows the superior feature to linear programming. In cleanup problem, genetic algorithm makes reasonable optimal design. Un this study, it is demonstrated through numerical experiments that genetic algorithm can be applied to remedial designs of groundwater problems.

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Fuzzy genetic algorithm for optimal control (최적 제어에 대한 퍼지 유전 알고리즘의 적용 연구)

  • 박정식;이태용
    • 제어로봇시스템학회:학술대회논문집
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    • pp.297-300
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    • 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.

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An Optimal Control of the Crane System Using a Genetic Algorithm (유전알고리즘을 이용한 크레인 시스템의 최적제어)

  • 최형식
    • Journal of Advanced Marine Engineering and Technology
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    • v.22 no.4
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    • pp.498-504
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    • 1998
  • This paper presents an optimal control algorithm for the overhead crane. To control the swing motion and the position tracking of the payload of the overhead crane a state feedback control algorithm is applied. by using a hybrid genetic algorithm the feedback gains of the state feedback is optimized to minimize the cost function composed of position errors and payload swing angle under unknown constant disturbances. Computer simulation is performed to demonstrate the effectiveness of the proposed control algorithm.

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Convergence Enhanced Successive Zooming Genetic Algorithm far Continuous Optimization Problems (연속 최적화 문제에 대한 수렴성이 개선된 순차적 주밍 유전자 알고리듬)

  • Gwon, Yeong-Du;Gwon, Sun-Beom;Gu, Nam-Seo;Jin, Seung-Bo
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.26 no.2
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    • pp.406-414
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    • 2002
  • A new approach, referred to as a successive zooming genetic algorithm (SZGA), is Proposed for identifying a global solution for continuous optimization problems. In order to improve the local fine-tuning capability of GA, we introduced a new method whereby the search space is zoomed around the design point with the best fitness per 100 generation. Furthermore, the reliability of the optimized solution is determined based on the theory of probability. To demonstrate the superiority of the proposed algorithm, a simple genetic algorithm, micro genetic algorithm, and the proposed algorithm were tested as regards for the minimization of a multiminima function as well as simple functions. The results confirmed that the proposed SZGA significantly improved the ability of the algorithm to identify a precise global minimum. As an example of structural optimization, the SZGA was applied to the optimal location of support points for weight minimization in the radial gate of a dam structure. The proposed algorithm identified a more exact optimum value than the standard genetic algorithms.