• Title/Summary/Keyword: Genetic Architecture

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Comparison of Genetic Algorithms and Simulated Annealing for Multiprocessor Task Allocation (멀티프로세서 태스크 할당을 위한 GA과 SA의 비교)

  • Park, Gyeong-Mo
    • The Transactions of the Korea Information Processing Society
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    • v.6 no.9
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    • pp.2311-2319
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    • 1999
  • We present two heuristic algorithms for the task allocation problem (NP-complete problem) in parallel computing. The problem is to find an optimal mapping of multiple communicating tasks of a parallel program onto the multiple processing nodes of a distributed-memory multicomputer. The purpose of mapping these tasks into the nodes of the target architecture is the minimization of parallel execution time without sacrificing solution quality. Many heuristic approaches have been employed to obtain satisfactory mapping. Our heuristics are based on genetic algorithms and simulated annealing. We formulate an objective function as a total computational cost for a mapping configuration, and evaluate the performance of our heuristic algorithms. We compare the quality of solutions and times derived by the random, greedy, genetic, and annealing algorithms. Our experimental findings from a simulation study of the allocation algorithms are presented.

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Genetic Algorithm based Methodology for an Single-Hop Metro WDM Networks

  • Yang, Hyo-Sik;Kim, Sung-Il;Shin, Wee-Jae
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • 2005.11a
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    • pp.306-309
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    • 2005
  • We consider the multi-objective optimization of a multi-service arrayed-waveguide grating-based single-hop metro WDM network with the two conflicting objectives of maximizing throughput while minimizing delay. We develop and evaluate a genetic algorithm based methodology for finding the optimal throughput-delay tradeoff curve, the so-called Pareto-optimal frontier. Our methodology provides the network architecture and the Medium Access Control protocol parameters that achieve the Pareto-optima in a computationally efficient manner. The numerical results obtained with our methodology provide the Pareto-optimal network planning and operation solution for a wide range of traffic scenarios. The presented methodology is applicable to other networks with a similar throughput-delay tradeoff.

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Genetically Optimized Fuzzy Polynomial Neural Networks Model and Its Application to Software Process (진화론적 최적 퍼지다항식 신경회로망 모델 및 소프트웨어 공정으로의 응용)

  • Lee, In-Tae;Park, Ho-Sung;Oh, Sung-Kwun;Ahn, Tae-Chon
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.337-339
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    • 2004
  • In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs). Proceeding the layer, this model creates the optimal network architecture through the selection and the elimination of nodes by itself. So, there is characteristic of flexibility. We use a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. GAs is applied to improve the performance with optimal input variables and number of input variables and order. To evaluate the performance of the GAs-based FPNNs, the models are experimented with the use of Medical Imaging System(MIS) data.

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The Study on the Optimum Design of Acoustic Interference Model by Genetic Algorithm (유전자 알고리즘을 이용한 음향 간섭 모델 최적화 설계)

  • Lee, Jae-Hwan;Jang, Kang-Seok
    • Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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    • 2003.10a
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    • pp.18-23
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    • 2003
  • The Objective of this research is to find the optimal design of the Noise Reduction Interference Model (NRIM) which is recently invented. Without optimization, the NRIM has been successful to reduce the urban noise induced by trains and automobiles. While it is used with the barrier on the road, there is a strong desire to reduce noise more. Yet the only remedy is to make the barrier higher with cost increase. Therefore, the optimal design is necessary to reduce noise while maintaining the barrier height. More efficient Genetic Algorithm is used to find the optimal shape of NRIM with the reduction of noise lever up to 15 dB. also BEM is used to verify the optimal design results.

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Dynamic Neural Units and Genetic Algorithms With Applications to the Control of Unknown Nonlinear Systems (동적 신경망과 Geneo-tic Algorithms를 적용한 비선형 시스템의 제어)

  • Cho, Hyun-Seob;Min, Jin-Kyoung;Roh, Yong-Gi;Jung, Byung-Jo;Jang, Sung-Whan
    • Proceedings of the KIEE Conference
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    • 2006.07d
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    • pp.1943-1944
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    • 2006
  • "Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin

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Dynamic Neural Units and Genetic Algorithms With Applications to the Control of Unknown Nonlinear Systems (Geneo-tic Algorithms을 이용한 비선형 동적 시스템 제어)

  • Kim, Hee-Sook;Park, Jong-Chun;Lee, Keun-Wang;Cho, Hyeon-Seob
    • Proceedings of the KIEE Conference
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    • 2004.07d
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    • pp.2484-2486
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    • 2004
  • "Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our method is different from those using supervised learning algorithms. such as the back propagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

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Evolvable Cellular Classifiers for pattern Recognition (패턴 인식을 위한 진화 셀룰라 분류기)

  • Ju, Jae-Ho;Shin, Yoon-Cheol;Kang, Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.4
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    • pp.379-389
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    • 2000
  • A cellular automaton is well-known for self-organizing and dynamic behavions in the filed of artifial life. This paper addresses a new neuronic architecture called an evolvable celluar classifier which evolves with the genetic rules (chromosomes) in the non-uniform cellular automata. An evolvable cellular classifier is primarily based on cellular programming, but its mechanism is simpler becaise it utilizes only mutations for the main genetic operators and resmbles the Hopfield network. Therefore, the desirable bit-patterns could be obtained through evolutionary processes for just one individual agent, As a rusult, an evolvable hardware is derived which is applicable to clessification of bit-string information.

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Vibration Control Performance Evaluation of Semi-active Outrigger Damper System (준능동 아웃리거 댐퍼시스템의 진동제어 성능평가)

  • Kim, Hyun-Su;Kang, Joo-Won
    • Journal of Korean Association for Spatial Structures
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    • v.15 no.4
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    • pp.81-89
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    • 2015
  • Damped outrigger systems have been proposed as a novel energy dissipation system to protect tall buildings from severe earthquakes and strong wind loads. In this study, semi-active damping devices such as magnetorheological (MR) dampers instead of passive dampers are installed vertically between the outrigger and perimeter columns to achieve large and adaptable energy dissipation. Control performance of semi-active outrigger damper system mainly depends on the control algorithm. Fuzzy logic control algorithm was used to generate command voltage sent to MR damper. Genetic algorithm was used to optimize the fuzzy logic controller. An artificial earthquake load was generated for numerical simulation. A simplified numerical model of damped outrigger system was developed. Based on numerical analyses, it has been shown that the semi-active damped outrigger system can effectively reduce both displacement and acceleration responses of the tall building in comparison with a passive outrigger damper system.

Dynamic Neural Units and Genetic Algorithms With Applications to the Control of Unknown Nonlinear Systems (Dynamic Neural Unit와 GA를 이용한 비선형 동적 시스템 제어)

  • Cho, Hyeon-Seob;Roh, Yong-Gi;Jang, Sung-Whan
    • Proceedings of the KAIS Fall Conference
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    • 2006.05a
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    • pp.311-315
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    • 2006
  • "Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin

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An approach to computerized preliminary design procedure of mid-size superyachts from hull modeling to interior space arrangement

  • Nam, Jong-Ho;Kim, Dong-Ham;Lee, Ho-Jin
    • International Journal of Naval Architecture and Ocean Engineering
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    • v.2 no.2
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    • pp.96-103
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    • 2010
  • A concept of preliminary design for mid-size superyachts is explored. First, the profile of a superyacht is interactively designed with the help of freeform curve functionality and graphical user interface (GUI) based interaction. The hull form is then constructed using major characteristic curves such as design waterline, deck sideline, and sections in addition to the predefined profile curve. After exterior hull modeling is done, the arrangement of significant interior spaces of all decks is carried out. A genetic algorithm is exploited to find a space arrangement by considering space fitness values, space proximity, and stairs connectivity of relevant spaces. A goal of the paper is to offer a step-by-step procedure for superyacht design from scratch or when initial information is not sufficient for complete design. For this purpose, a GUI based superyacht design system is developed. This design approach is expected to help users interactively design mid-size superyachts.