• Title/Summary/Keyword: genetic algorithm(GA)

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The Efficient Method of Parallel Genetic Algorithm using MapReduce of Big Data (빅 데이터의 MapReduce를 이용한 효율적인 병렬 유전자 알고리즘 기법)

  • Hong, Sung-Sam;Han, Myung-Mook
    • Journal of the Korean Institute of Intelligent Systems
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    • v.23 no.5
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    • pp.385-391
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    • 2013
  • Big Data is data of big size which is not processed, collected, stored, searched, analyzed by the existing database management system. The parallel genetic algorithm using the Hadoop for BigData technology is easily realized by implementing GA(Genetic Algorithm) using MapReduce in the Hadoop Distribution System. The previous study that the genetic algorithm using MapReduce is proposed suitable transforming for the GA by MapReduce. However, they did not show good performance because of frequently occurring data input and output. In this paper, we proposed the MRPGA(MapReduce Parallel Genetic Algorithm) using improvement Map and Reduce process and the parallel processing characteristic of MapReduce. The optimal solution can be found by using the topology, migration of parallel genetic algorithm and local search algorithm. The convergence speed of the proposal method is 1.5 times faster than that of the existing MapReduce SGA, and is the optimal solution can be found quickly by the number of sub-generation iteration. In addition, the MRPGA is able to improve the processing and analysis performance of Big Data technology.

Design and Implementation of a Genetic Algorithm for Detailed Routing (디테일드 라우팅 유전자 알고리즘의 설계와 구현)

  • 송호정;송기용
    • Journal of the Institute of Convergence Signal Processing
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    • v.3 no.3
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    • pp.63-69
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    • 2002
  • Detailed routing is a problem assigning each net to a track after global routing. The most popular algorithms for detailed routing include left-edge algorithm, dogleg algorithm, and greedy channel routing algorithm. In this paper we propose a genetic algorithm searching solution space for the detailed routing problem. We compare the performance of proposed genetic algorithm(GA) for detailed routing with that of greedy channel routing algorithm by analyzing the results of each implementation.

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Design and Implementation of a Genetic Algorithm for Global Routing (글로벌 라우팅 유전자 알고리즘의 설계와 구현)

  • 송호정;송기용
    • Journal of the Institute of Convergence Signal Processing
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    • v.3 no.2
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    • pp.89-95
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    • 2002
  • Global routing is to assign each net to routing regions to accomplish the required interconnections. The most popular algorithms for global routing inlcude maze routing algorithm, line-probe algorithm, shortest path based algorithm, and Steiner tree based algorithm. In this paper we propose weighted network heuristic(WNH) as a minimal Steiner tree search method in a routing graph and a genetic algorithm based on WNH for the global routing. We compare the genetic algorithm(GA) with simulated annealing(SA) by analyzing the results of each implementation.

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A Study on The Restoration of Substation using Genetic Algorithm (유전 알고리즘을 이용한 변전소 복구 방안에 관한 연구)

  • Park, Young-Moon;Won, Jong-Ryul
    • Proceedings of the KIEE Conference
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    • 1996.07b
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    • pp.820-822
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    • 1996
  • This paper proposes a method for seeking the scheme of substation restoration by using genetic algorithm. Genetic algorithm (GA), first introduced by John Holland, is becoming an important tool in machine learning and function optimization. GA is a searching or optimization algorithm based on Darwinian biological evolution principle. As a test system, we assume a simple substation system and for the transformer fault, the result is obtained.

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Implementation of a Genetic Operator for Genetic Algorithm (유전자 알고리즘의 유전 연산자 구현)

  • You, Myoung-Keun;Song, Gi-Yong
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • 2005.11a
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    • pp.357-360
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    • 2005
  • 유전자 알고리즘(Genetic Algorithm, GA)은 자연적 진화과정에서 생존 경쟁 측면의 가장 적합한 메커니즘이다. GA를 소프트웨어로 수행하는데 큰 지연시간은 필수적이기 때문에 하드웨어 설계를 이용하여 알고리즘 실행 속도를 증가시키기 위한 많은 연구가 진행되어 왔다. 본 논문에서는 염색체의 임의의 유전인자를 기준으로 입력 받은 염색체에 대하여 GA 연산을 수행하는 유전 연산자를 설계한다. 설계된 디자인을 ARM 코어와 PLD로 구성된 Altera사의 Excalibur칩에 구현하여 동작을 검증하였다.

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Micro Genetic Algorithms in Structural Optimization and Their Applications (마이크로 유전알고리즘을 이용한 구조최적설계 및 응용에 관한 연구)

  • 김종헌;이종수;이형주;구본홍
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2002.04a
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    • pp.225-232
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    • 2002
  • Simple genetic algorithm(SGA) has been used to optimize a lot of structural optimization problems because it can optimize non-linear problems and obtain the global solution. But, because of large evolving populations during many generations, it takes a long time to calculate fitness. Therefore this paper applied micro-genetic algorithm(μ -GA) to structural optimization and compared results of μ -GA with results of SGA. Additionally, the Paper applied μ -GA to gate optimization problem for injection molds by using simulation program CAPA.

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Real-Time Power-Saving Scheduling Based on Genetic Algorithms in Multi-core Hybrid Memory Environments (멀티코어 이기종메모리 환경에서의 유전 알고리즘 기반 실시간 전력 절감 스케줄링)

  • Yoo, Suhyeon;Jo, Yewon;Cho, Kyung-Woon;Bahn, Hyokyung
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.20 no.1
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    • pp.135-140
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    • 2020
  • Recently, due to the rapid diffusion of intelligent systems and IoT technologies, power saving techniques in real-time embedded systems has become important. In this paper, we propose P-GA (Parallel Genetic Algorithm), a scheduling algorithm aims at reducing the power consumption of real-time systems in multi-core hybrid memory environments. P-GA improves the Proportional-Fairness (PF) algorithm devised for multi-core environments by combining the dynamic voltage/frequency scaling of the processor with the nonvolatile memory technologies. Specifically, P-GA applies genetic algorithms for optimizing the voltage and frequency modes of processors and the memory types, thereby minimizing the power consumptions of the task set. Simulation experiments show that the power consumption of P-GA is reduced by 2.85 times compared to the conventional schemes.

Nonlinear Blind Equalizer Using Hybrid Genetic Algorithm and RBF Networks

  • Han, Soo-Whan;Han, Chang-Wook
    • Journal of Korea Multimedia Society
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    • v.9 no.12
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    • pp.1689-1699
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    • 2006
  • A nonlinear channel blind equalizer by using a hybrid genetic algorithm, which merges a genetic algorithm with simulated annealing, and a RBF network is presented. In this study, a hybrid genetic algorithm is used to estimate the output states of a nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. From these estimated output states, the desired channel states of the nonlinear channel are derived and placed at the center of a RBF equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm(GA) and a simplex GA, and the relatively high accuracy and fast convergence of the method are achieved.

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SA-selection-based Genetic Algorithm for the Design of Fuzzy Controller

  • Han Chang-Wook;Park Jung-Il
    • International Journal of Control, Automation, and Systems
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    • v.3 no.2
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    • pp.236-243
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    • 2005
  • This paper presents a new stochastic approach for solving combinatorial optimization problems by using a new selection method, i.e. SA-selection, in genetic algorithm (GA). This approach combines GA with simulated annealing (SA) to improve the performance of GA. GA and SA have complementary strengths and weaknesses. While GA explores the search space by means of population of search points, it suffers from poor convergence properties. SA, by contrast, has good convergence properties, but it cannot explore the search space by means of population. However, SA does employ a completely local selection strategy where the current candidate and the new modification are evaluated and compared. To verify the effectiveness of the proposed method, the optimization of a fuzzy controller for balancing an inverted pendulum on a cart is considered.

Solving Facility Rearrangement Problem Using a Genetic Algorithm and a Heuristic Local Search

  • Suzuki, Atsushi;Yamamoto, Hisashi
    • Industrial Engineering and Management Systems
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    • v.11 no.2
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    • pp.170-175
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    • 2012
  • In this paper, a procedure using a genetic algorithm (GA) and a heuristic local search (HLS) is proposed for solving facility rearrangement problem (FRP). FRP is a decision problem for stopping/running of facilities and integration of stopped facilities to running facilities to maximize the production capacity of running facilities under the cost constraint. FRP is formulated as an integer programming model for maximizing the total production capacity under the constraint of the total facility operating cost. In the cases of 90 percent of cost constraint and more than 20 facilities, the previous solving method was not effective. To find effective alternatives, this solving procedure using a GA and a HLS is developed. Stopping/running of facilities are searched by GA. The shifting the production operation of stopped facilities into running facilities is searched by HLS, and this local search is executed for one individual in this GA procedure. The effectiveness of the proposed procedure using a GA and HLS is demonstrated by numerical experiment.