• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.341-346
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• 2004

Nowadays, versatile robots are developed around the world. Novel algorithms are needed for controlling such robots. A 2-Layer fuzzy controller can deal with many inputs as well as many outputs, and its overall structure is much simpler than that of a general fuzzy controller. The main problem encountered in fuzzy control is the design of the fuzzy controller. In this paper, the fuzzy controller is designed by the schema co-evolutionary algorithm. This algorithm can quickly and easily find a global solution. Therefore, the schema co-evolutionary algorithm is used to design a 2-layer fuzzy controller in this study. We apply it to a mobile robot and verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm through the experiments.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.166-168
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• 1996

This paper presents an power system optimization method which solves real and reactive power dispatch problems using evolutionary computation such as genetic algorithms(GAs), evolutionary programming(EP), and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most these approaches have the common defect of being caught to a local minimum solution. Recently, global search methods such as GAs, EP, and ES are introduced. The proposed methods, applied to the IEEE 30-bus system, were run for 12 other exogenous parameters. Each simulation result, by which evolutionary computations are compared and analyzed, shows the possibility of applications of evolutionary computation to large scale power systems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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• pp.1092-1098
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• 2002

This paper discusses the composition of the theory of reinforcement teaming, which is applied in real-time teaming, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line teaming method. The individuals are reduced in order to team the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It is 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 of the teaming 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.

• 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.

• Journal of The Korean Association For Science Education
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• v.31 no.3
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• pp.440-454
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• 2011

This study proposes a new instructional strategy and corresponding materials designed from various alternative frameworks to help students understand evolution as a biologically acceptable theory. Biology teachers have normally taught the evolutionary mechanism by means of comparing Lamarckism with natural selection. In this study, a new instructional strategy in which the Lamarckian explanation is first excluded because Lamarckism is known to be subsumed in a learner's cognitive structure as a strong preconception of evolution is suggested for teaching evolution. After mutation theory is introduced, Darwinism including natural selection is explained separately during the next class hour. Corresponding instructional materials that aid student understanding of the evolutionary mechanism were developed using recently published articles on human genetic traits as scientific evolutionary evidence instead of the traditional evolutionary subject matter, giraffe neck. Evolutionary evidence from human genetic traits allows students to exclude anthropocentric thoughts effectively and raise concern for the phenomenon of evolution positively. The administered instructional strategy and materials in this research improved student conception, concern, and belief of evolution and it is believed that they helped students understand the evolutionary mechanism effectively.

• Korean Journal of Biological Psychiatry
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• v.24 no.2
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• pp.45-51
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• 2017

Anxiety is one of the basic human emotions. From the evolutionary psychology point of view, it is a necessary factor for survival and prosperity of human beings that had been developed throughout time with the history of human survival and development. Anxiety plays the role of protecting one from social or physical threats. In reality, lacking of anxiety showed lots of examples of maladjustments. But the result of over-adjustment, which is overanxious disorder, is definitely disturbing one's survival and growth, and it can lead to anxiety disorder that needs to be treated. Anxiety from the evolutionary psychology point of view, started as a primary adjustment form and it evolves into various types of anxiety disorders that relates to the modern society's characters. Therefore, having the grasp of evolutionary psychology, which can be the base of treating anxiety and anxiety disorders, is very important. So from now on, studies for this aspect would need to be done as integrated and multidisciplinary studies not only by psychiatrists, but by including epidemiologists, psychologists, ecologists, biologists, and neuropsychologists. In this article, the author tried to review and explore the idea of anxiety and anxiety disorders from the evolutionary psychology point of view.

• 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.

• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.119-124
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• 1999

This paper proposes an evolutionary model for automatically generating artificial creatures of various shapes and colors according to insect ecology. This model offers a novel way to naturally evolve the shapes and colors of artificial creatures. The evolutionary model used in our research is based on Genetic Algorithms (GA). In this paper, artificial Computer Graphics(CG) creatures develop into various shapes and colors according to the evolutionary model. Later, they can be used as CG animated characters. This model also solves the problem of reducing the time and labor cost for mass production of various characters. It could be used in such areas as the cavalry battle scene in Disney's animation, “Mulan”. Our approach has two steps. At first, artificial creatures move according to information gathered form the five senses. This information is also used for generating the shapes of the five sense organs[1]. Then, based on the GA, evolutionary mode[2], we prepare prototype creatures, which evolve into various shapes and different colors in alternating generations. Finally, our evolutionary model successfully generates various character shapes and colors automatically.

• 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.