• Title/Summary/Keyword: a co-evolution

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Co-Evolution of Fuzzy Rules and Membership Functions

  • Jun, Hyo-Byung;Joung, Chi-Sun;Sim, Kwee-Bo
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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    • pp.601-606
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    • 1998
  • In this paper, we propose a new design method of an optimal fuzzy logic controller using co-evolutionary concept. In general, it is very difficult to find optimal fuzzy rules by experience when the input and/or output variables are going to increase. Futhermore proper fuzzy partitioning is not deterministic ad there is no unique solution. So we propose a co-evolutionary method finding optimal fuzzy rules and proper fuzzy membership functions at the same time. Predator-Prey co-evolution and symbiotic co-evolution algorithms, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. Our algorithm is that after constructing two population groups made up of rule base and membership function, by co-evolving these two populations, we find optimal fuzzy logic controller. By applying the propose method to a path planning problem of autonomous mobile robots when moving objects applying the proposed method to a pa h planning problem of autonomous mobile robots when moving objects exist, we show the validity of the proposed method.

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Treatability Study on Oil-Contaminated Soils for Bioremediation Application (유류오염토양의 생물적용기술 적용타당성 검토)

  • Lee, Yeon-Hui;Seol, Mi-Jin;O, Yeong-Suk
    • 한국생물공학회:학술대회논문집
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    • 2001.11a
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    • pp.578-581
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    • 2001
  • A treatability study was conducted using a hydrocarbon-contaminated soil for the oPtimization of bioremediation strategy best fit to a given set of contamination. The applicability of nutrients, biosurfactant, and oil-degrading microorganisms were examined by monitoring $CO_2$ evolution and oil degradation The addition of inorganic nutrients in the form of slow released fertilizer accelerated the initial rate of $CO_2$ evolution by a factor of 3. The application of oil-degrading microorganisms did not significantly increased $CO_2$ evolution or biodegradation efficiency. Application of a commercial biosurfactant was most effect in terms of the total $CO_2$ evolution and the oil degradation rate. The results indicate that $CO_2$ evolution measurement was found to be a simple and reliable countermeasure of crude oil hydrocarbon mineralization for the rapid determination of the best-fit bioremediation strategy.

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Observation of Bargaining Game using Co-evolution between Particle Swarm Optimization and Differential Evolution (입자군집최적화와 차분진화알고리즘 간의 공진화를 활용한 교섭게임 관찰)

  • Lee, Sangwook
    • The Journal of the Korea Contents Association
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    • v.14 no.11
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    • pp.549-557
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    • 2014
  • Recently, analysis of bargaining game using evolutionary computation is essential issues in field of game theory. In this paper, we observe a bargaining game using co-evolution between two heterogenous artificial agents. In oder to model two artificial agents, we use a particle swarm optimization and a differential evolution. We investigate algorithm parameters for the best performance and observe that which strategy is better in the bargaining game under the co-evolution between two heterogenous artificial agents. Experimental simulation results show that particle swarm optimization outperforms differential evolution in the bargaining game.

Technology Intelligence based on the Co-evolution Analysis : Semiconductor Package Process Case (공진화 분석기반 기술 인텔리전스 : 반도체 패키지공정 사례)

  • Lee, Byungjoon;Shin, Juneseuk
    • Journal of Technology Innovation
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    • v.28 no.4
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    • pp.63-93
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    • 2020
  • We suggest a new way of specifying the co-evolution of product and process technologies, and integrating it into one of the well-received technology intelligence tools - a technology radar. Cross impact analysis enables us to identify the core technologies of product-process co-evolution. Combining expert judgment with its results, we can clarify the technological co-evolution trajectory with mainstream as well as emerging core technologies. Reflecting these in the assessment process of a technology radar, we could improve reliance of the technology assessment process and technology portfolio. From the academic perspective, our research provides a point where the co-evolution theory encouners technology intelligence methods. Practically, strategic capability of future-preparedness and strategic management could improve by adopting our method based on our example of co-evolution of semiconductor product and process technologies.

Co-Evolutionary Algorithm and Extended Schema Theorem

  • Sim, Kwee-Bo;Jun, Hyo-Byung
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.2 no.1
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    • pp.95-110
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    • 1998
  • Evolutionary Algorithms (EAs) are population-based optimization methods based on the principle of Darwinian natural selection. The representative methodology in EAs is genetic algorithm (GA) proposed by J. H. Holland, and the theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. In the meaning of these foundational concepts, simple genetic algorithm (SGA) allocate more trials to the schemata whose average fitness remains above average. Although SGA does well in many applications as an optimization method, still it does not guarantee the convergence of a global optimum in GA-hard problems and deceptive problems. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve in contrast with traditional single population evolutionary algorithm. In this paper we show why the co-evolutionary algorithm works better than SGA in terms of an extended schema theorem. And predator-prey co-evolution and symbiotic co-evolution, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. And the experimental results show a co-evolutionary algorithm works well in optimization problems even though in deceptive functions.

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Structure optimization of neural network using co-evolution (공진화를 이용한 신경회로망의 구조 최적화)

  • 전효병;김대준;심귀보
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.4
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    • pp.67-75
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    • 1998
  • In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.

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A study on the structure evolution of neural networks using genetic algorithms (유전자 알고리즘을 이용한 신경회로망의 구조 진화에 관한 연구)

  • 김대준;이상환;심귀보
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.223-226
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    • 1997
  • Usually, the Evolutionary Algorithms(EAs) are considered more efficient for optimal, system design because EAs can provide higher opportunity for obtaining the global optimal solution. This paper presents a mechanism of co-evolution consists of the two genetic algorithms(GAs). This mechanism includes host populations and parasite populations. These two populations are closely related to each other, and the parasite populations plays an important role of searching for useful schema in host populations. Host population represented by feedforward neural network and the result of co-evolution we will find the optimal structure of the neural network. We used the genetic algorithm that search the structure of the feedforward neural network, and evolution strategies which train the weight of neuron, and optimize the net structure. The validity and effectiveness of the proposed method is exemplified on the stabilization and position control of the inverted-pendulum system.

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Effect of Photosynthesis on Ozone-Induced Ethylent Evolution from Tomato Plants (토마토 식물에 있어서 광합성이 유존유동성의 에틸렌 생성에 미치는 영향)

  • 배공영
    • Journal of Korean Society for Atmospheric Environment
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    • v.12 no.3
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    • pp.307-314
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    • 1996
  • The rate of evolution of ethylent by tomato plants was rapidly increased by ozone fumigation. In the present study, the mechanism of ethylent evolution by ozone was investigated in experiments with aminoethoxyvinylglycine (AVG) and tiron, which inhibit the formation of ethylene and peroxidation of lipids, respectively. Pretreatment with AVG significantly inhibited the ozone-induced ethylent evolution, but the treatment of plants with tiron did not inhibit. These results indicate that the induction of the evolution of ethylene by ozone involves the pathway via aminocyclopropane-1-carboxylate (ACC), while not released as a result of the peroxidation of lipids. Ozone-induced ethylent evolution was greater in dar- than light-incubated, intact tomato plants. The difference between dark- and light-ethylene evolution was examined with diuron, an inhibitor of photosynthetic electron transport. The inhibitor treatment promoted ethylent evolution. These results suggest that ethylent retention and metabolism in plants were regulated by internal $CO_2$ levels which, in turn, were controlled in large part by photosynthesis. Thus, ethylene was retained in illuminated leaf tissue under low intenal $CO_2$ concentration which may develop in a sealed container without exogenously supplied $CO_2$.

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Co-Evolution between Open Innovation and Absorptive Capacity in Korean SMEs (개방형 혁신과 흡수역량의 공진화 : 한국 중소기업의 혁신경로 관점)

  • Sohn, Dong-Won
    • Korean Management Science Review
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    • v.29 no.3
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    • pp.169-182
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    • 2012
  • This study examines the co-evolutionary process between open innovation and firms' absorptive capacity. The effects of open innovation can be maximized through the capacity to absorb the knowledge from the external sources such as universities, government-support research institute, and private R&D centers. This study used data of STEPI technology innovation survey conducted at 2002, 2005, and 2008 (3 points measures). The data were analyzed through a structural equation model. Results suggest that open innovation at t0 point influences positively the absorptive capacity at t1 point, which subsequently enhances the intention of open innovation at t2 point. This result suggests the existence of co-evolutionary process between open innovation and firms' absorptive capacity. When knowledge comes from universities, the co-evolution has sustained; whereas when knowledge comes from private firms' R&D centers, the co-evolution has not effected. Theoretical and practical implications are discussed.

An Architecture Supporting Adaptation and Evolution in Fourth Generation Mobile Communication Systems

  • Prehofer, Christian;Kellerer, Wolfgang;Hirschfeld, Robert;Berndt, Hendrik;Kawamura, Katsuya
    • Journal of Communications and Networks
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    • v.4 no.4
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    • pp.336-343
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    • 2002
  • A major challenge for next generation mobile communication is capturing the system architecture’s complexity with all its internal and external dependencies. Seamless integration of heterogeneous environments in all system parts is a key requirement. Moreover, future systems have to consider the different evolution cycles of individual system parts. Among those, services are expected to change the fastest. With respect to these considerations, we propose an overall architecture for next generation mobile communication systems. It covers all system parts from wireless transmission to applications including network and middleware platform. Our approach focuses on adaptability in terms of recon- figurability and programmability to support unanticipated system evolution. Therefore, we consider abstraction layers which consist of adaptable cooperating components grouped by open platforms rather than rigid system layers. In addition to that, we introduce cross-layer cooperation allowing an efficient use of the available resources. Specific scenarios illustrate the feasibility of our approach.