• Title/Summary/Keyword: Co-evolutionary

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Design of Scenario Creation Model for AI-CGF based on Naval Operations, Resources Analysis Model(I): Evolutionary Learning (해군분석모델용 AI-CGF를 위한 시나리오 생성 모델 설계(I): 진화학습)

  • Hyun-geun, Kim;Jung-seok, Gang;Kang-moon, Park;Jae-U, Kim;Jang-hyun, Kim;Bum-joon, Park;Sung-do, Chi
    • Journal of the Korea Institute of Military Science and Technology
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    • v.25 no.6
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    • pp.617-627
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    • 2022
  • Military training is an essential item for the fundamental problem of war. However, there has always been a problem that many resources are consumed, causing spatial and environmental pollution. The concepts of defense modeling and simulation and CGF(Computer Generated Force) using computer technology began to appear to improve this problem. The Naval Operations, Resources Analysis Model(NORAM) developed by the Republic of Korea Navy is also a DEVS(Discrete Event Simulation)-based naval virtual force analysis model. The current NORAM is a battle experiment conducted by an operator, and parameter values such as maneuver and armament operation for individual objects for each situation are evaluated. In spite of our research conducted evolutionary, supervised, reinforcement learning, in this paper, we introduce our design of a scenario creation model based on evolutionary learning using genetic algorithms. For verification, the NORAM is loaded with our model to analyze wartime engagements. Human-level tactical scenario creation capability is secured by automatically generating enemy tactical scenarios for human-designed Blue Army tactical scenarios.

Schema Analysis on Co-Evolutionary Algorithm (공진화 알고리즘에 있어서 스키마 해석)

  • Kwee-Bo Sim;Hyo-Byung Jun
    • Journal of Institute of Control, Robotics and Systems
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    • v.4 no.5
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    • pp.616-623
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    • 1998
  • Holland가 제안한 단순 유전자 알고리즘은 다원의 자연선택설을 기본으로 한 군 기반의 최적화 방법으로서, 이론적 기반으로는 스키마 정리와 빌딩블록 가설이 있다. 단순 유전자 알고리즘(SGA)이 이러한 이론적 기반에도 불구하고 여전히 일부 문제에 있어서 최적해로의 수렴을 보장하지 못하고 있다. 따라서 최근에 두 개의 집단이 서로 상호작용을 하며 진화하는 공진화 방법에 의해 이러한 문제를 해결하려고 하는데 많은 관심이 모아지고 있다. 본 논문에서는 이러한 공진화 방법이 잘 동작하는지에 대한 이론적 기반으로 확장 스키마 정리를 제안하고, SGA에서는 해결하지 못하는 최적화 문제, 예를 들면 deceptive function,에서 SGA와 공진화에 의한 방법을 비교함으로써 확장된 스키마 정리의 유효성을 확인한다.

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Co-evolutionary Structural Design Framework: Min(Volume Minimization)-Max(Critical Load) MOD Problem of Topology Design under Uncertainty (구조-하중 설계를 고려한 공진화 구조 설계시스템)

  • 양영순;유원선;김봉재
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2003.04a
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    • pp.335-347
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    • 2003
  • 본 논문에서는 설계 하중에 지배되는 구조물에 있어서, 입력 파라미터들의 불확실성을 표준편차와 패턴의 변동, 두 차원에서 접근, 처리할 수 있는 방안을 제시하기 위해서 구조물에 입력으로 작용하는 하중 패턴의 결정과 구조물의 형상의 진화를 동시에 고려할 수 있는 Co-Evolutionary Structural Design framework라 명명한 새로운 구조 설계 방식을 개발하였다. 공학자의 직관과 경험 의존적인 하중을 대상으로 최적화된 구조물은, 성능에 완벽한 안전을 보장해 줄 수 없으며, 이에 관한 문제를 해결하기 위해서 주어진 상황 속에서 다양한 하중이 작용하더라도 안전할 수 있는 구조물의 설계 방식에 관해서 설명한다. 본 프레임워크는 연성을 가지는 두 Disciplinary Modules, 즉 구조 형상설계와 하중설계로 이루어지며 하중에 관한 DB로 연결되어 순차적인 MDO 설계과정을 거치게 된다. 두 Discipline은 설계과정을 거치면서 상호 견제의 틀 속에서 진화하며 기존 방식과 달리 극한 하중 패턴을 스스로 찾아서 설계 반영하는 특징을 가진다. 본 접근 방식의 유용성을 평가하기 위해서 10-bar truss 구조물과 Jacket-Type 구조물로 테스트해 보았다.

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Gene Expression Analysis by Co-evolutionary Biclustering (유전자 발현 분석을 위한 공진화적 바이클러스터링 기법)

  • Joung Je-Gun;Kim Soo-Jin;Zhang Byoung-Tak
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.06a
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    • pp.22-24
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    • 2006
  • 마이크로어레이는 전체 유전체 수준의 mRNA 발현 여부에 대한 측정이 가능하다는 점에서 분자생물학의 실험 도구로서 가장 강력한 도구 중에 하나로 부각되어 있다. 현재까지 마이크로어래이의 결과로부터 유사한 발현 패턴을 찾기 위한 여러 가지 바이클러스터링 알고리즘들이 개발되어 왔다. 하지만 대다수의 알고리즘들이 최적의 바이클러스터들을 찾기보다는 일정 수준의 가능한 바이클러스터의 결과만을 제시하고 있다. 본 논문에서는 다른 개체집단들과 상호 진화하는 공진화적 학습에 의한 진화연산 기법을 통하여 유전자-조건의 매트릭스로부터 열과 행을 동시에 클러스터링하는 공진화적 바이클러스터링 알고리즘(co-evolutionary biclustering algorithm: CBA)을 제안하고자 한다. CBA는 유전자발현 데이터에서 유전자-조건의 상호의존적인 부성분들로 구성된 최적화 문제에 적합한 계산방식이라고 할 수 있다. 인간 유전자 발현 데이터에 대한 실험 결과. 제시한 알고리즘은 이전의 알고리즘에 비해 발견한 바이클러스터의 패턴 유사도에 있어서 우수한 성능을 보이고 있다.

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Co-evolutionary Structural Design Framework: Min(Volume Minimization)-Max(Critical Load) MDO Problem of Topology Design under Uncertainty (구조-하중 설계를 고려한 공진화 구조 설계시스템)

  • 양영순;유원선;김봉재
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.16 no.3
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    • pp.281-290
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    • 2003
  • Co Evolutionary Structural Design(CESD) Framework is presented, which can deal with the load design and structural topology design simultaneously. The load design here is the exploration algorithm that finds the critical load patterns of the given structure. In general, the load pattern is a crucial factor in determining the structural topology and being selected from the experts어 intuition and experience. However, if any of the critical load patterns would be excluded during the process of problem formation, the solution structure might show inadequate performance under the load pattern. Otherwise if some reinforcement method such as safety factor method would be utilized, the solution structure could result in inefficient conservativeness. On the other hand, the CESD has the ability of automatically finding the most critical load patterns and can help the structural solution evolve into the robust design. The CESD is made up of a load design discipline and a structural topology design discipline both of which have the fully coupled relation each other. This coupling is resolved iteratively until the resultant solution can resist against all the possible load patterns and both disciplines evolve into the solution structure with the mutual help or competition. To verify the usefulness of this approach, the 10 bar truss and the jacket type offshore structure are presented. SORA(Sequential Optimization & Reliability Assessment) is adopted in CESD as a probabilistic optimization methodology, and its usefulness in decreasing the computational cost is verified also.

Analysis of Evolutionary Content in High School Biology Textbook (고등학교 생물 교과서에서의 진화내용분석)

  • Kim, Hak-Hyun;Chang, Nam-Kee
    • Journal of The Korean Association For Science Education
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    • v.23 no.5
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    • pp.470-483
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    • 2003
  • This study analyzed the evolutionary content in 13 textbooks developed from the first to the 6th high school biology curriculum, The content analysis of textbooks, which were delineated nine component, was performed on the 80 evolutionary categories, According to the result, the proportion of the total evolutionary content in textbook increased from the textbooks developed by the Ist curriculum to the textbooks developed by the 6th curriculum, but the proportion of 'main narrative' in total evolutionary content was gradually decreased. It also showed that biology curriculum and points of view of textbook writers influenced on the proportion of evolutionary contents. On the whole, the topics of analysed textbooks exhibit insufficient diversity, Any categories- 'group selection', 'gene selection', 'gaps in fossil record', 'co-evolution', 'punctuated equilibrium', 'mosaic evolution', 'place of labor in human evolution', 'human race differentiation', 'criticism of "ontogeny recapitulates phylogeny", and 'human activities affecting evolution' - were not treated and others - 'theory of neutralism', 'theories of major episodes(excepting extinctions) found in the geologic time scale', 'sympatric speciation', 'clinal and area-effect speciation', 'polyploidy and evolution', 'gradualism' and 'evolution and origin of mammals' - were treated very lightly, the most emphasized topic was 'phylogeny in general' and 'formation of precells', 'miscellaneous' in the order of emphasis. 'Theory of natural selection' was lightly treated as just one of evolutionary theory though it should be emphasized as major theme of evolution. Also, the law of recapitulation, of which biologists doubt the validity, was discussed as an evidence of evolution in some textbooks. And the agents of genetic equilibrium disruption like genetic drift and migration were treated as of little importance. On the basis of above result, it was suggested that the textbook writers introduced the more meaningful evolutionary topics focused the theory of natural selection in explanation of evolution and evolution theory.

CHEMICAL EVOLUTION OF INTERSTELLAR CLOUDS AND VARIATIONS OF MOLECULAR ABUNDANCES

  • Minn, Y.K.
    • Journal of The Korean Astronomical Society
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    • v.13 no.1
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    • pp.9-14
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    • 1980
  • The abundances of simple molecules are examined in terms of the time-dependent cloud evolution. The formation and destruction mechanisms of $H_2CO$ are reviewed. The average value of the fractional abundance of $H_2CO$ is derived to be in the range of $10^{-10}\;to\;5{\times}10^{-9}$. This is comparable to the observed values. The expected variations of the molecules formed from or destroyed by CO, CI, and $C^+$ whose abundances depend on the evolutionary state of the cloud are discussed.

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A new method for an automated synthesis of heat exchanger networks (열교환망 자동합성을 위한 새로운 방법)

  • Lee, Gyu-Hwang;Kim, Min-Seok;Lee, In-Beom;Go, Hong-Cheol
    • Journal of Institute of Control, Robotics and Systems
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    • v.4 no.2
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    • pp.256-263
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    • 1998
  • Among process synthesis problems, the heat exchanger network (HEN) has been subjected to the most concentrated effort because this kind of problems was well defined for solving it and 20-30% energy savings could be realized in the present chemical processes. In this paper, we use an evolutionary approach for HEN synthesis because this approach can overcome the local optimum and combine some heuristic rules. The basic evolutionary approach is composed of three parts, that is, initialization step, growth step and mutation step, as in the simulated annealing and genetic algorithm. This algorithm uses the ecological rule that a better cell will live and worse cell should decompose after repeated generations. With this basic concept, a new procedure is developed and a more efficient method is proposed to generate initial solutions. Its effectiveness is shown using test examples.

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Evolution of Neural Network's Structure and Learn Patterns Based on Competitive Co-Evolutionary Method (경쟁적 공진화법에 의한 신경망의 구조와 학습패턴의 진화)

  • Joung, Chi-Sun;Lee, Dong-Wook;Jun, Hyo-Byung;Sim, Kwee-Bo
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.36S no.1
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    • pp.29-37
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    • 1999
  • In general, the information processing capability of a neural network is determined by its architecture and efficient training patterns. However, there is no systematic method for designing neural network and selecting effective training patterns. Evolutionary Algorithms(EAs) are referred to as the methods of population-based optimization. Therefore, EAs are considered as very efficient methods of optimal system design because they can provide much opportunity for obtaining the global optimal solution. In this paper, we propose a new method for finding the optimal structure of neural networks based on competitive co-evolution, which has two different populations. Each population is called the primary population and the secondary population respectively. The former is composed of the architecture of neural network and the latter is composed of training patterns. These two populations co-evolve competitively each other, that is, the training patterns will evolve to become more difficult for learning of neural networks and the architecture of neural networks will evolve to learn this patterns. This method prevents the system from the limitation of the performance by random design of neural networks and inadequate selection of training patterns. In co-evolutionary method, it is difficult to monitor the progress of co-evolution because the fitness of individuals varies dynamically. So, we also introduce the measurement method. The validity and effectiveness of the proposed method are inspected by applying it to the visual servoing of robot manipulators.

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Robust Gain Scheduling Based on Fuzzy Logic Control and LMI Methods (퍼지논리제어와 LMI기법을 이용한 강인 게인 스케줄링)

  • Chi, Hyo-Seon;Koo, Kuen-Mo;Lee, Hungu;Tahk, Min-Jea;Hong, Sung-Kyung
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.1
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    • pp.1162-1170
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    • 2001
  • This paper proposes a practical gain-scheduling control law considering robust stability and performance of Linear Parameter Varying(LPV) systems in the presence of nonlinearities and uncertainties. The proposed method introduces LMI-based pole placement synthesis and also associates with a recently developed fuzzy control system based on Takagei-Sugenos fuzzy model. The sufficient conditions for robust controller design of linearized local dynamics and robust stabilization of fuzzy control systems are reduced to a finite set of Linear Matrix inequalities(LMIs) and solved by using co-evolutionary algorithms. The proposed method is applied to the longitudinal acceleration control of high performance aircraft with linear and nonlinear simulations.

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