• Title, Summary, Keyword: Genetic Algorithm

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Real-time processing system for embedded hardware genetic algorithm (임베디드 하드웨어 유전자 알고리즘을 위한 실시간 처리 시스템)

  • Park Se-hyun;Seo Ki-sung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.8 no.7
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    • pp.1553-1557
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    • 2004
  • A real-time processing system for embedded hardware genetic algorithm is suggested. In order to operate basic module of genetic algorithm in parallel, such as selection, crossover, mutation and evaluation, dual processors based architecture is implemented. The system consists of two Xscale processors and two FPGA with evolvable hardware, which enables to process genetic algorithm efficiently by distributing the computational load of hardware genetic algorithm to each processors equally. The hardware genetic algorithm runs on Linux OS and the resulted chromosome is executed on evolvable hardware in FPGA. Furthermore, the suggested architecture can be extended easily for a couple of connected processors in serial, making it accelerate to compute a real-time hardware genetic algorithm. To investigate the effect of proposed approach, performance comparisons is experimented for an typical computation of genetic algorithm.

A Water-saving Irrigation Decision-making Model for Greenhouse Tomatoes based on Genetic Optimization T-S Fuzzy Neural Network

  • Chen, Zhili;Zhao, Chunjiang;Wu, Huarui;Miao, Yisheng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.6
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    • pp.2925-2948
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    • 2019
  • In order to improve the utilization of irrigation water resources of greenhouse tomatoes, a water-saving irrigation decision-making model based on genetic optimization T-S fuzzy neural network is proposed in this paper. The main work are as follows: Firstly, the traditional genetic algorithm is optimized by introducing the constraint operator and update operator of the Krill herd (KH) algorithm. Secondly, the weights and thresholds of T-S fuzzy neural network are optimized by using the improved genetic algorithm. Finally, on the basis of the real data set, the genetic optimization T-S fuzzy neural network is used to simulate and predict the irrigation volume for greenhouse tomatoes. The performance of the genetic algorithm improved T-S fuzzy neural network (GA-TSFNN), the traditional T-S fuzzy neural network algorithm (TSFNN), BP neural network algorithm(BPNN) and the genetic algorithm improved BP neural network algorithm (GA-BPNN) is compared by simulation. The simulation experiment results show that compared with the TSFNN, BPNN and the GA-BPNN, the error of the GA-TSFNN between the predicted value and the actual value of the irrigation volume is smaller, and the proposed method has a better prediction effect. This paper provides new ideas for the water-saving irrigation decision in greenhouse tomatoes.

An Optimization Technique For Crane Acceleration Using A Genetic Algorithm (유전자알고리즘을 이용한 크레인가속도 최적화)

  • 박창권;김재량;정원지;홍대선;권장렬;박범석
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.1701-1704
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    • 2003
  • This paper presents a new optimization technique of acceleration curve for a wafer transfer crane movement in which high speed and low vibration are desirable. This technique is based on a genetic algorithm with a penalty function for acceleration optimization under the assumption that an initial profile of acceleration curves constitutes the first generation of the genetic algorithm. Especially the penalty function consists of the violation of constraints and the number of violated constraints. The proposed penalty function makes the convergence rate of optimization process using the genetic algorithm more faster than the case of genetic algorithm without a penalty function. The optimized acceleration of the crane through the genetic algorithm and commercial dynamic analysis software has shown to have accurate movement and low vibration.

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Multimodal Optimization Based on Global and Local Mutation Operators

  • Jo, Yong-Gun;Lee, Hong-Gi;Sim, Kwee-Bo;Kang, Hoon
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1283-1286
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    • 2005
  • Multimodal optimization is one of the most interesting topics in evolutionary computational discipline. Simple genetic algorithm, a basic and good-performance genetic algorithm, shows bad performance on multimodal problems, taking long generation time to obtain the optimum, converging on the local extrema in early generation. In this paper, we propose a new genetic algorithm with two new genetic mutational operators, i.e. global and local mutation operators, and no genetic crossover. The proposed algorithm is similar to Simple GA and the two genetic operators are as simple as the conventional mutation. They just mutate the genes from left or right end of a chromosome till the randomly selected gene is replaced. In fact, two operators are identical with each other except for the direction where they are applied. Their roles of shaking the population (global searching) and fine tuning (local searching) make the diversity of the individuals being maintained through the entire generation. The proposed algorithm is, therefore, robust and powerful.

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A Hybrid Genetic Algorithm for Job Shop Scheduling (Job Shop 일정계획을 위한 혼합 유전 알고리즘)

  • 박병주;김현수
    • Journal of the Korean Operations Research and Management Science Society
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    • v.26 no.2
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    • pp.59-68
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    • 2001
  • The job shop scheduling problem is not only NP-hard, but is one of the well known hardest combinatorial optimization problems. The goal of this research is to develop an efficient scheduling method based on hybrid genetic algorithm to address job shop scheduling problem. In this scheduling method, generating method of initial population, new genetic operator, selection method are developed. The scheduling method based on genetic algorithm are tested on standard benchmark job shop scheduling problem. The results were compared with another genetic algorithm0-based scheduling method. Compared to traditional genetic, algorithm, the proposed approach yields significant improvement at a solution.

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Optimal Design of Laminated Stiffened Composite Structures using a parallel micro Genetic Algorithm (병렬 마이크로 유전자 알고리즘을 이용한 복합재 적층 구조물의 최적설계)

  • Yi, Moo-Keun;Kim, Chun-Gon
    • Composites Research
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    • v.21 no.1
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    • pp.30-39
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    • 2008
  • In this paper, a parallel micro genetic algorithm was utilized in the optimal design of composite structures instead of a conventional genetic algorithm(SGA). Micro genetic algorithm searches the optimal design variables with only 5 individuals. The diversities from the nominal convergence and the re-initialization processes make micro genetic algorithm to find out the optimums with such a small population size. Two different composite structure optimization problems were proposed to confirm the efficiency of micro genetic algorithm compared with SGA. The results showed that micro genetic algorithm can get the solutions of the same level of SGA while reducing the calculation costs up to 70% of SGA. The composite laminated structure optimization under the load uncertainty was conducted using micro genetic algorithm. The result revealed that the design variables regarding the load uncertainty are less sensitive to load variation than that of fixed applied load. From the above-mentioned results, we confirmed micro genetic algorithm as a optimization method of composite structures is efficient.

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 Balancing at Two-sided and Mixed Model Work Line Using Genetic Algorithm (효율적인 유전알고리듬을 이용하여 양면.혼합모델 작업라인 균형에 대한 연구)

  • 이내형;조남호
    • Proceedings of the Safety Management and Science Conference
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    • pp.91-97
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    • 2002
  • In this thesis presents line balancing problems of two-sided and mixed model assembly line widely used in practical fields using genetic algorithm for reducing throughput time, cost of tools and fixtures and improving flexibility of assembly lines. Two-sided and mixed model assembly line is a special type of production line where variety of product similar in product characteristics are assembled in both sides. This thesis proposes the genetic algorithm adequate to each step in tow-sided and mixed model assembly line with suitable presentation, individual, evaluation function, selection and genetic parameter. To confirm proposed genetic algorithm, we apply to increase the number of tasks in case study. And for evaluation the performance of proposed genetic algorithm, we compare to existing algorithm of one-sided and mixed model assembly line. The results show that the algorithm is outstanding in the problems with a larger number of stations or larger number of tasks.

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A Study on the Optimal Trajectory Planning for a Ship Using Genetic algorithm (유전 알고리즘을 이용한 선박의 최적 항로 결정에 관한 연구)

  • 이병결;김종화;김대영;김태훈
    • 제어로봇시스템학회:학술대회논문집
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    • pp.255-255
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    • 2000
  • Technical advance of electrical chart and cruising equipment make it possible to sail without a man. It is important to decide the cruising route in view of effectiveness and stability of a ship. So we need to study on the optimal trajectory planning. Genetic algorithm is a strong optimization algorithm with adaptational random search. It is a good choice to apply genetic algorithm to the trajectory planning of a ship. We modify a genetic algorithm to solve this problem. The effectiveness of the revised genetic algorithm is assured through computer simulations.

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Direction Vector for Efficient Structural Optimization with Genetic Algorithm (효율적 구조최적화를 위한 유전자 알고리즘의 방향벡터)

  • Lee, Hong-Woo
    • Journal of Korean Association for Spatial Structures
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    • v.8 no.3
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    • pp.75-82
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    • 2008
  • In this study, the modified genetic algorithm, D-GA, is proposed. D-GA is a hybrid genetic algorithm combined a simple genetic algorithm and the local search algorithm using direction vectors. Also, two types of direction vectors, learning direction vector and random direction vector, are defined without the sensitivity analysis. The accuracy of D-GA is compared with that of simple genetic algorithm. It is demonstrated that the proposed approach can be an effective optimization technique through a minimum weight structural optimization of ten bar truss.

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