• Title/Summary/Keyword: genetic evolutionary algorithm

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

  • Yuh, Baeg-Youh;Park, Choon-Wook;Kang, Moon-Myung
    • Journal of Korean Association for Spatial Structures
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    • v.2 no.3 s.5
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    • pp.93-102
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    • 2002
  • The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. 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 algorithms. 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 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 size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

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Size, Shape and Topology Optimum Design of Trusses Using Shape & Topology Genetic Algorithms (Shape & Topology GAs에 의한 트러스의 단면, 형상 및 위상최적설계)

  • Park, Choon-Wook;Yuh, Baeg-Youh;Kim, Su-Won
    • 한국공간정보시스템학회:학술대회논문집
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    • 2004.05a
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    • pp.43-52
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    • 2004
  • The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algerian 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 algorithms. 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 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 size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

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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.

An Analysis of the Evolution of a Fuzzy Logic Controller using Evolutionary Activity (진화활동성을 이용한 퍼지 제어기의 진화 분석)

  • 이승익;조성배
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2001.05a
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    • pp.113-116
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    • 2001
  • This paper analyzes the evolutionary process of a fuzzy logic controller using evolutionary activity. An evolutionary algorithm is commonly used to find solutions for given problems. However, little has been done on the analysis of the evolutionary pathways to the optimal solutions. This paper uses a genetic algorithm to construct a fuzzy logic controller for a mobile robot and applies evolutionary activity to measure the adaptability quantitatively. Evolutionary activity can be defined as the rate at which useful genetic innovations are absorbed in the population. By measuring the evolutionary activities, we will show quantitatively that the optimal fuzzy logic controller is not from other genetic phenomena like chance or necessity, but from the adaptability to a given encironment.

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Implementation of GA Processor for Efficient Sequence Generation (효율적인 DNA 서열 생성을 위한 진화연산 프로세서 구현)

  • Jeon, Sung-Mo;Kim, Tae-Seon;Lee, Chong-Ho
    • Proceedings of the KIEE Conference
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    • 2003.11c
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    • pp.376-379
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    • 2003
  • DNA computing based DNA sequence Is operated through the biology experiment. Biology experiment used as operator causes illegal reactions through shifted hybridization, mismatched hybridization, undesired hybridization of the DNA sequence. So, it is essential to design DNA sequence to minimize the potential errors. This paper proposes method of the DNA sequence generation based evolutionary operation processor. Genetic algorithm was used for evolutionary operation and extra hardware, namely genetic algorithm processor was implemented for solving repeated evolutionary process that causes much computation time. To show efficiency of the Proposed processor, excellent result is confirmed by comparing between fitness of the DNA sequence formed randomly and DNA sequence formed by genetic algorithm processor. Proposed genetic algorithm processor can reduce the time and expense for preparing DNA sequence that is essential in DNA computing. Also it can apply design of the oligomer for development of the DNA chip or oligo chip.

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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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Design of Fuzzy Controller Using Parasitic Co-evolutionary Algorithm (기생적 공진화 알고리즘을 이용한 퍼지 제어기 설계)

  • 심귀보;변광섭
    • Journal of Institute of Control, Robotics and Systems
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    • v.10 no.11
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    • pp.1071-1076
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    • 2004
  • It is a fuzzy controller that it is the most used method in the control of non-linear system. The most important part in the fuzzy controller is a design of fuzzy rules. Many algorithm that design fuzzy rules have proposed. And attention to the evolutionary computation is increasing in the recent days. Among them, the co-evolutionary algorithm is used in the design of optimal fuzzy rule. This paper takes advantage of a schema co-evolutionary algorithm. In order to verify the efficiency of the schema co-evolutionary algorithm, a fuzzy controller for the mobile robot control is designed by the schema co-evolutionary algorithm and it is compared with other parasitic co-evolutionary algorithm such as a virus-evolutionary genetic algorithm and a co-evolutionary method of Handa.

Evolutionary Optimization Design Technique for Control of Solid-Fluid Coupled Force (고체-유체 연성력 제어를 위한 진화적 최적설계)

  • Kim H.S.;Lee Y.S.
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2005.06a
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    • pp.503-506
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    • 2005
  • In this study, optimization design technique for control of solid-fluid coupled force (sloshing) using evolutionary method is suggested. Artificial neural networks(ANN) and genetic algorithm(GA) is employed as evolutionary optimization method. The ANN is used to analysis of the sloshing and the genetic algorithm is adopted as an optimization algorithm. In the creation of ANN learning data, the design of experiments is adopted to higher performance of the ANN learning using minimum learning data and ALE(Arbitrary Lagrangian Eulerian) numerical method is used to obtain the sloshing analysis results. The proposed optimization technique is applied to the minimization of sloshing of the water in the tank lorry with baffles under 2 second lane change.

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Sloshing Reduction Optimization of Storage Tank Using Evolutionary Method (진화적 기법을 이용한 유체저장탱크의 슬로싱 저감 최적화)

  • 김현수;이영신;김승중;김영완
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2004.05a
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    • pp.410-415
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    • 2004
  • The oscillation of the fluid caused by external forces is call ed sloshing, which occurs in moving vehicles with contained liquid masses, such as trucks, railroad cars, aircraft, and liquid rocket. This sloshing effect could be a severe problem in vehicle stability and control. In this study, the optimization design technique for reduction of the sloshing using evolutionary method is suggested. Two evolutionary methods are employed, respectively the artificial neural network(ANN) and genetic algorithm. An artificial neural network is used for the analysis of sloshing and genetic algorithm is adopted as optimization algorithm. As a result of optimization design, the optimized size and location of the baffle is presented

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Design Application of Evolutionary Algorithms in Architecture (디지털 디자인 미디어 - Evolutionary Algorithms의 현대건축에의 적용 방법론)

  • Kim, Ho-Jeong
    • Journal of Industrial Technology
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    • v.27 no.A
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    • pp.39-46
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    • 2007
  • I discuss the preliminary version of an investigative software, GSE, - Genetic 3D Surface Explorer, in which genetic operations interact with AutoCAD to generate novel 3D Forms for the Architect. GSE allows us to comment on design issues concerning computer aided design tools based on evolutionary algorithms.

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