• Journal of Technology Innovation
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• v.13 no.1
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• pp.213-246
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• 2005

Despite of the impressive progress made in the Evolutionary techno-economics during the last two decades, there have been very little, if not at all, theoretical advancement in explaining an endogenous mechanism of transforming a technological paradigm within self-perpetuatingstructural dynamics. The question poorly attempted was raised by Schumpeter a century ago in his effort to overcome the well-known 'Walras' trap'. Although there have been increasing number of researchers recently tackling the issue quite seriously from within the Evolutionary school, I see it that radical reconstruction of the basic principle of Evolutionary research framework is urgently needed to solve the century long fundamental question, from evolutionary approach to transformational approach. In the paper, I will show the theoretical feasibility of explaining an endogenous mechanism of paradigm transformation, relying upon the concept of localized dynamics and the concept of morphogenetic structuration. It should be emphasized that there must be aendogenous process of deepening structural Instability generated in the process of economic coordination to secure efficient circular flow. The concept of development bottleneck initiated by the Baumol's cost disease could be regarded as one of the important source of such mechanism. Unfortunately, however, it is a brief conceptual description presented in the paper rather than a comprehensive analytical model, due to the space limitation imposed.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.7
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• pp.424-433
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.7
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• pp.424-424
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.65-77
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• 2003

This paper addresses FMS(Flexible Manufacturing System) loading problems with machine, tool and process flexibilities. When designing FMS planning, it is important to take account of these flexibilities for an efficient utilization of the resources. However, almost all the existing researches do not appropriately consider various flexibilities due to the problem complexity. This paper presents a new evolutionary algorithm to solve the FMS loading problems with machine, tool and process flexibilities. The algorithm is named a multi-level symbiotic evolutionary algorithm. The proposed algorithm is compared with the existing ones in terms of solution quality and convergence speed. The experimental results confirm the effectiveness of our approach.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.87-98
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• 2002

A competitive coevolutionary algorithm is a probabilistic search method that imitates coevolution process through evolutionary arms race. The algorithm has been used to solve adversarial problems. In the algorithms, the selection of competitors is needed to evaluate the fitness of an individual. The goal of this study is to compare and analyze several competition strategies in terms of solution quality, convergence speed, balance between competitive coevolving species, population diversity, etc. With two types of test-bed problems, game problems and solution-test problems, extensive experiments are carried out. In the game problems, sampling strategies based on fitness have a risk of providing bad solutions due to evolutionary unbalance between species. On the other hand, in the solution-test problems, evolutionary unbalance does not appear in any strategies and the strategies using information about competition results are efficient in solution quality. The experimental results indicate that the tournament competition can progress an evolutionary arms race and then is successful from the viewpoint of evolutionary computation.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.105-109
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• 2001

This paper discuss the real-time self-tuning learning control based on evolutionary computation, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.724-729
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• 2001

This paper discusses the composition of the theory of reinforcement learning, which is applied in real-time learning, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.9
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• pp.1463-1468
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• 2003

This paper presented the real-time self-tuning learning control based on evolutionary computation, which proves its superiority in finding of the optimal solution at the off-line learning method. The individuals of the populations are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations is proposed. It is possible to control the control object slightly varied as time changes. As the state value of the control object is generated, evolutionary strategy is applied each sampling time because the learning process of an estimation, selection, mutation is done in real-time. These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.68.1-68.1
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• 2015

Gravitational collapse is a dynamical process associated with star formation. One observational evidence of such infall motion is so called "blue asymmetry" profile, which is the optically thick line profile with the intensity peak skewed blueward relative to the intensity peak of optically thin lines. We analyzed both HCN J=1-0 and HNC J=1-0 line profiles to study the inflow motion in different evolutionary stages of massive star formation; Infrared dark clouds (IRDCs), High-mass protostellar object (HMPOs), and Ultra-compact HII regions (UCHIIs). The infall asymmetry in the HCN spectra seems to be more prevalent than the HNC spectra throughout all the three evolutionary phases. The prevalence of the blue profile in the HCN spectra is found in every evolutionary stage, with IRDCs showing the largest blue excess. In the case of the HNC spectra, only IRDCs show the blue excess statistically significant. These results suggest that HCN may be a better infall tracer in massive star forming region. In addition, even though the characteristics of the blue profile largely depend on the suitable combination of optical depth and critical density, our analyses also indicate that IRDCs may have the most active infall process compared to other evolutionary phases.

• Culinary science and hospitality research
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• v.24 no.1
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• pp.122-129
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• 2018

The purpose of this study is to understand the evolution of food culture by applying the evolutionary mechanism to the process of forming the dog meat culture. To do this, this study first examined mutation, selection, and replication as a evolutionary mechanism by biological genes and explain the evolutionary process of food culture by applying so-called 'mime' which is a virally-transmitted cultural symbol or social idea. A meme acts as a unit for carrying cultural ideas, practices, that can be transmitted from one mind to another through writing, speech, gestures, rituals, or other imitable phenomena with a mimicked theme. In addition, this study also intended to use in-depth interviews on how people have diverse cultural perspectives interpret and accept edible culture of dog meat. In Korea, which was a traditional farming society, dog meat which is easier to obtain compare to beef has been chosen as an important source of protein. And this choice has been repeatedly reproduced through generations. However, the current generation's awareness of the edible culture of dog meat has changed. The meme of pet culture has been selected and replicated, and this cultural evolution will eventually lead to the culling of dog meat.