• Title, Summary, Keyword: Genetic Algorithm

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Optimization of Fuzzy Car Controller Using Genetic Algorithm

  • Kim, Bong-Gi;Song, Jin-Kook;Shin, Chang-Doon
    • Journal of information and communication convergence engineering
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    • v.6 no.2
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    • pp.222-227
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    • 2008
  • The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

Parameter Identification of an Electro-Hydraulic Servo System Using an Improved Hybrid Neural-Genetic Multimodel Algorithm (개선된 신경망-유전자 다중모델에 의한 전기.유압 서보시스템의 파라미터 식별)

  • 곽동훈;정봉호;이춘태;이진걸
    • Journal of the Korean Society for Precision Engineering
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    • v.20 no.5
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    • pp.196-203
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    • 2003
  • This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm, The ICRA neural network evaluates each member of a generation of model and the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.

Parameter Identification Using Hybrid Neural-Genetic Algorithm in Electro-Hydraulic Servo System (신경망-유전자 알고리즘을 이용한 전기${\cdot}$유압 서보시스템의 파라미터 식별)

  • 곽동훈;정봉호;이춘태;이진걸
    • Journal of the Korean Society for Precision Engineering
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    • v.19 no.11
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    • pp.192-199
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    • 2002
  • This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

Parameter Identification of an Electro-Hydraulic Servo System Using a Modified Hybrid Neural-Genetic Algorithm (전기.유압 서보시스템의 수정된 신경망-유전자 알고리즘에 의한 파라미터 식별)

  • 곽동훈;이춘태;정봉호;이진걸
    • Journal of Institute of Control, Robotics and Systems
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    • v.9 no.6
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    • pp.442-447
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    • 2003
  • This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.

A Study on the Two-sided and Mixed Model Assembly Line Balancing Using Genetic Algorithm (유전알고리듬을 이용한 양면.혼합모델 조립라인 밸런싱)

  • 이내형;조남호
    • Journal of the Korea Safety Management & Science
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    • v.4 no.2
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    • pp.83-101
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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.

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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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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A Fast Anti-jamming Decision Method Based on the Rule-Reduced Genetic Algorithm

  • Hui, Jin;Xiaoqin, Song;Miao, Wang;Yingtao, Niu;Ke, Li
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.9
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    • pp.4549-4567
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    • 2016
  • To cope with the complex electromagnetic environment of wireless communication systems, anti-jamming decision methods are necessary to keep the reliability of communication. Basing on the rule-reduced genetic algorithm (RRGA), an anti-jamming decision method is proposed in this paper to adapt to the fast channel variations. Firstly, the reduced decision rules are obtained according to the rough set (RS) theory. Secondly, the randomly generated initial population of the genetic algorithm (GA) is screened and the individuals are preserved in accordance with the reduced decision rules. Finally, the initial population after screening is utilized in the genetic algorithm to optimize the communication parameters. In order to remove the dependency on the weights, this paper deploys an anti-jamming decision objective function, which aims at maximizing the normalized transmission rate under the constraints of minimizing the normalized transmitting power with the pre-defined bit error rate (BER). Simulations are carried out to verify the performance of both the traditional genetic algorithm and the adaptive genetic algorithm. Simulation results show that the convergence rates of the two algorithms increase significantly thanks to the initial population determined by the reduced-rules, without losing the accuracy of the decision-making. Meanwhile, the weight-independent objective function makes the algorithm more practical than the traditional methods.

Reduction of Air-pumping Noise based on a Genetic Algorithm (유전자 알고리즘을 이용한 타이어 공력소음의 저감)

  • Kim, Eui-Youl;Hwang, Sung-Wook;Kim, Byung-Hyun;Lee, Sang-Kwon
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.22 no.1
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    • pp.61-73
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    • 2012
  • The paper presents the novel approach to solve some problems occurred in application of the genetic algorithm to the determination of the optimal tire pattern sequence in order to reduce the tire air-pumping noise which is generated by the repeated compression and expansion of the air cavity between tire pattern and road surface. The genetic algorithm has been used to find the optimal tire pattern sequence having a low level of tire air-pumping noise using the image based air-pumping model. In the genetic algorithm used in the previous researches, there are some problems in the encoding structure and the selection of objective function. The paper proposed single encoding element with five integers, divergent objective function based on evolutionary process and the optimal evolutionary rate based on Shannon entropy to solve the problems. The results of the proposed genetic algorithm with evolutionary process are compared with those of the randomized algorithm without evolutionary process on the two-dimensional normal distribution. It is confirmed that the genetic algorithm is more effective to reduce the peak value of the predicted tire air-pumping noise and the consistency and cohesion of the obtained simulation results are also improved in terms of probability.

Shape & Topology Optimum Design of Truss Structures Using Genetic Algorithms (유전자 알고리즘에 의한 트러스의 형상 및 위상최적실계)

  • Park, Choon Wook;Youh, Baeg Yuh;Kang, Moon Myung
    • Journal of Korean Society of Steel Construction
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    • v.13 no.6
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    • pp.673-681
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    • 2001
  • The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithm. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithm. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the design points selected form the genetic process. The evolutionary process evaluates the survivability of the design points. The evolutionary process evaluates the survivability of the design points selected form the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithm was verified by applying the algorithm to optimum design examples.

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