• Title, Summary, Keyword: Genetic Algorithms

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Distributed Genetic Algorithms for the TSP (분산 유전알고리즘의 TSP 적용)

  • 박유석
    • Journal of the Korea Safety Management and Science
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    • v.3 no.3
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    • pp.191-200
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    • 2001
  • Parallel Genetic Algorithms partition the whole population into several sub-populations and search the optimal solution by exchanging the information each others periodically. Distributed Genetic Algorithm, one of Parallel Genetic Algorithms, divides a large population into several sub-populations and executes the traditional Genetic Algorithm on each sub-population independently. And periodically promising individuals selected from sub-populations are migrated by following the migration interval and migration rate to different sub-populations. In this paper, for the Travelling Salesman Problems, we analyze and compare with Distributed Genetic Algorithms using different Genetic Algorithms and using same Genetic Algorithms on each separated sub-population The simulation result shows that using different Genetic Algorithms obtains better results than using same Genetic Algorithms in Distributed Genetic Algorithms. This results look like the property of rapidly searching the approximated optima and keeping the variety of solution make interaction in different Genetic Algorithms.

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Discrete Optimum Design of Space Truss Structures Using Genetic Algorithms

  • Park, Choon Wook;Kang, Moon Myung
    • Architectural research
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    • v.4 no.1
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    • pp.33-38
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    • 2002
  • The objective of this study is the development of discrete optimum design algorithms which is based on the genetic algorithms. The developed algorithms was implemented in a computer program. For the optimum design, the objective function is the weight of space trusses structures and the constraints are stresses and displacements. This study solves the problem by introducing the genetic algorithms. The genetic algorithms consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed discrete optimum design algorithms was verified by applying the algorithms to optimum design examples.

Competitive Generation for Genetic Algorithms

  • Jung, Sung-Hoon
    • Journal of Korean Institute of Intelligent Systems
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    • v.17 no.1
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    • pp.86-93
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    • 2007
  • A new operation termed competitive generation in the processes of genetic algorithms is proposed for accelerating the optimization speed of genetic algorithms. The competitive generation devised by considering the competition of sperms for fertilization provides a good opportunity for the genetic algorithms to approach global optimum without falling into local optimum. Experimental results with typical problems showed that the genetic algorithms with competitive generation are superior to those without the competitive generation.

A Study on Adaptive Random Signal-Based Learning Employing Genetic Algorithms and Simulated Annealing (유전 알고리즘과 시뮬레이티드 어닐링이 적용된 적응 랜덤 신호 기반 학습에 관한 연구)

  • Han, Chang-Wook;Park, Jung-Il
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.10
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    • pp.819-826
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    • 2001
  • Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

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A study of ball-beam system control using genetic algorithms (유전자 알고리즘을 이용한 Ball-Beam 시스템의 제어에 관한 연구)

  • Lee, Nam-Gi;Park, Jong-Beom;Cho, Hwang
    • 제어로봇시스템학회:학술대회논문집
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    • pp.968-971
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    • 1996
  • In this paper, feedback controller is designed for ball-beam system using genetic algorithms. A genetic algorithms are implemented for optimizing gain parameters of feedback controller. We can find optimal point in multi-dimensional search space by using genetic algorithms. Performance of controller is tested by simulation of ball-beam system.

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Optimal Configuration of Distribution Network using Genetic Algorithms

  • Kim, Intaek;Wonhyuk Cho
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.625-628
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    • 1998
  • This paper presents an application of genetic algorithms(GAs) for optimal configuration of distribution network. Three problems have been used to show how genetic algorithms are modified and applied. Solutions to the problems are found by minimizing the cost function which is directly related with balancing the loads. Simulation results show that genetic algorithms are technically feasible if they are tailored to meet the needs of real problems.

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Fusion of Genetic Algorithms and Fuzzy Inference System (유전 알고리즘과퍼지 푸론 시스템의 합성)

  • 황희수;오성권;우광방
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.41 no.9
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    • pp.1095-1103
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    • 1992
  • An approach to fuse the fuzzy inference system which is able to deal with imprecise and uncertain information and genetic algorithms which display the excellent robustness in complex optimization problems is presented in this paper. In order to combine genetic algorithms and fuzzy inference engine effectively the new reasoning method is suggested. The efficient identification method of fuzzy rules is proposed through the adjustment of search areas of genetic algorithms. The feasibilty of the proposed approach is evaluated through simulation.

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Derivative Evaluation and Conditional Random Selection for Accelerating Genetic Algorithms

  • Jung, Sung-Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.5 no.1
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    • pp.21-28
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    • 2005
  • This paper proposes a new method for accelerating the search speed of genetic algorithms by taking derivative evaluation and conditional random selection into account in their evolution process. Derivative evaluation makes genetic algorithms focus on the individuals whose fitness is rapidly increased. This accelerates the search speed of genetic algorithms by enhancing exploitation like steepest descent methods but also increases the possibility of a premature convergence that means most individuals after a few generations approach to local optima. On the other hand, derivative evaluation under a premature convergence helps genetic algorithms escape the local optima by enhancing exploration. If GAs fall into a premature convergence, random selection is used in order to help escaping local optimum, but its effects are not large. We experimented our method with one combinatorial problem and five complex function optimization problems. Experimental results showed that our method was superior to the simple genetic algorithm especially when the search space is large.

Fast Optimization by Queen-bee Evolution and Derivative Evaluation in Genetic Algorithms

  • Jung, Sung-Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.5 no.4
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    • pp.310-315
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    • 2005
  • This paper proposes a fast optimization method by combining queen-bee evolution and derivative evaluation in genetic algorithms. These two operations make it possible for genetic algorithms to focus on highly fitted individuals and rapidly evolved individuals, respectively. Even though the two operations can also increase the probability that genetic algorithms fall into premature convergence phenomenon, that can be controlled by strong mutation rates. That is, the two operations and the strong mutation strengthen exploitation and exploration of the genetic algorithms, respectively. As a result, the genetic algorithm employing queen-bee evolution and derivative evaluation finds optimum solutions more quickly than those employing one of them. This was proved by experiments with one pattern matching problem and two function optimization problems.

Optimum Design of Trusses Using Genetic Algorithms (유전자 알고리즘을 이용한 트러스의 최적설계)

  • 김봉익;권중현
    • Journal of Ocean Engineering and Technology
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    • v.17 no.6
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    • pp.53-57
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    • 2003
  • Optimum design of most structural system requires that design variables are regarded as discrete quantities. This paper presents the use of Genetic Algorithm for determining the optimum design for truss with discrete variables. Genetic Algorithm are know as heuristic search algorithms, and are effective global search methods for discrete optimization. In this paper, Elitism and the method of conferring penalty parameters in the design variables, in order to achieve improved fitness in the reproduction process, is used in the Genetic Algorithm. A 10-Bar plane truss and a 25-Bar space truss are used for discrete optimization. These structures are designed for stress and displacement constraints, but buckling is not considered. In particular, we obtain continuous solution using Genetic Algorithms for a 10-bar truss, compared with other results. The effectiveness of Genetic Algorithms for global optimization is demonstrated through two truss examples.