• Korean Journal of Cognitive Science
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• v.28 no.3
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• pp.173-191
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• 2017

The theories of cultural evolution hold subtly or clearly different stances about definition of culture, pattern of cultural evolution, biases that affect cultural evolution, and relationship between culture and organism. However, the cultural evolution theories have a common problem to solve: As the evolutionary theory of life tries to explain the early steps and the origin of life, the cultural evolution theories also must explain the early steps of the cultural evolution and the role of the human capability that makes cultural evolution possible. Therefore, explanations of the human's unique traits including the cultural ability are related to determine which one is the most plausible among many cultural evolution theories. Theories that tried to explain human uniqueness commonly depict the coevolution of gene (organism) and culture. We will explicitly call the niche construction theory and the dual inheritance theory the 'gene-culture co-evolutionary theory'. In these theories, the most important concept is the 'concept of positive feedback'. In this paper, we distinguish between core positive feedback and marginal positive feedback, according to whether the trait that the concept of positive feedback explains is the trait of human uniqueness. Both types of positive feedback effectively explain the generality of human uniqueness and the diversity of human traits driven by cultural groups. However, this positive feedback requires an end, in contrast to negative feedback which can be continued in order to maintain homeostasis. We argue that the co-evolutionary process in the gene-culture co-evolutionary theories include only the positive feedback, not covering the cultural evolution after the positive feedback. This thesis strives to define the coevolution concept more comprehensively by suggesting the potential relationships between gene and culture after the positive feedback.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.122-123
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• 2008

• 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.

• Journal of Korean Society for Quality Management
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• v.39 no.1
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• pp.98-108
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• 2011

Clustering is an useful method to classify objects into subsets that have some meaning in the context of a particular problem and has been applied in variety of fields, customer relationship management, data mining, pattern recognition, and biotechnology etc. This paper addresses the unknown K clustering problems and presents a new approach based on a coevolutionary algorithm to solve it. Coevolutionary algorithms are known as very efficient tools to solve the integrated optimization problems with high degree of complexity compared to classical ones. The problem considered in this paper can be divided into two sub-problems; finding the number of clusters and classifying the data into these clusters. To apply to coevolutionary algorithm, the framework of algorithm and genetic elements suitable for the sub-problems are proposed. Also, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. To analyze the proposed algorithm, the experiments are performed with various test-bed problems which are grouped into several classes. The experimental results confirm the effectiveness of the proposed algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.158-163
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• 1998

This paper is our first attempt to construct a information processing system such as the living creatures' brain based on artificial life technique. In this paper, we propose a method of constructing neural networks using bio-inspired emergent and evolutionary concept, Ontogeny of living things is realized by cellular automata model and Phylogeny that is living things adaptation ability themselves to given environment, are realized by evolutionary algorithms. Proposing evolving cellular automata neural systems are calledin a word ECANS. A basic component of ECANS is 'cell' which is modeled on chaotic neuron with complex characteristics, In our system, the states of cell are classified into eight by method of connection neighborhood cells. When a problem is given, ECANS adapt itself to the problem by evolutionary method. For fixed cells transition rule, the structure of neural network is adapted by change of initial cell' arrangement. This initial cell is to become a network b developmental process. The effectiveness and the capability of proposed scheme are verified by applying it to pattern classification and robot control problem.

• Journal of ILASS-Korea
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• v.25 no.3
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• pp.111-118
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• 2020

This paper describes the effect of DME, biodiesel blended fuels on the macroscopic spray characteristics in a high pressure diesel injection system using Background Oriented Schlieren (BOS) method. The BOS method for visualization of impingement evaporation sprays to analyze macroscopic spray properties and evolutionary processes. In this work, the blending ratio of DME in the blended fuel are 0, 50, 100% by weight ratio. In order to investigate the macroscopic impinged spray characteristics under the various injection parameters and blending ratio. In this work, a mini-sac type single-hole nozzle injector with nozzle hole was length 0.7 mm and diameter of 0.3 mm was used. According to the result, the spray area of the collision wall increased as the DME mixing ratio increased, and the evolutionary pattern showed a stepwise increase due to the collision effect of the wall. Also, results of impinged spray area were increased according to increasing injection pressure.

• Journal of Mechanical Science and Technology
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• v.14 no.11
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• pp.1206-1215
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• 2000

This paper introduces the application of evolutionary fuzzy modeling (EFM) in constructing global function approximations to subsequent use in non-gradient based optimizations strategies. The fuzzy logic is employed for express the relationship between input training pattern in form of linguistic fuzzy rules. EFM is used to determine the optimal values of membership function parameters by adapting fuzzy rules available. In the study, genetic algorithms (GA's) treat a set of membership function parameters as design variables and evolve them until the mean square error between defuzzified outputs and actual target values are minimized. We also discuss the enhanced accuracy of function approximations, comparing with traditional response surface methods by using polynomial interpolation and back propagation neural networks in its ability to handle the typical benchmark problems.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.43.2-43
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• 2001

In this paper, we consider the problems of the evolutionary designed neural networks structures by genetic algorithm. Neural networks has been applied to various application fields since back-propagation algorithm was proposed, e.g. function approximation, pattern or character recognition and so on. However, one of difficulties to use the neural networks. It is how to design the structure of the neural network. Researchers and users design networks structures and training parameters such as learning rate and momentum rate and so on, by trial and error based on their experiences. In the case of designing large scales neural networks, it is very hard work for manually design by try and error. For this difficulty, various structural learning algorithms have been proposed. Especially, the technique of using genetic algorithm for networks structures design has been ...

• Journal of Korea Game Society
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• v.11 no.2
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• pp.57-68
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• 2011

This study approaches how newly emerged game companies has implemented strategies for product diversification according to market growth cycle(beginninggrowing-mature) by empirical case study through evolutionary theory and resource based theory approach. At the beginning, online game companies had grown with different strategies(technology based, service based) by initial condition(genre, technological level, user attribute). After market growth, for product diversification, these companies carried out path-dependent alliance strategy(complementary, competitive) depending on resource base(technology capacity, service capacity based). As online game market getting mature, these companies has adapted flexibly in responding to market growth cycle by integrated strategy(naturally selected to mobilize every possible resource capability). By analyzing the alliance strategies pattern of online game companies in newly emerged game industry according to market growth cycle through combination of resource based theory and evolutionary theory, these results suggest that new industrial, theoretical, policy model is required.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.120-128
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• 2010

A hypernetwork is a generalized hypo-graph and a probabilistic graphical model based on evolutionary learning. Hypernetwork models have been applied to various domains including pattern recognition and bioinformatics. Nevertheless, conventional hypernetwork models have the limitation that they can manage data with categorical or discrete attibutes only since the learning method of hypernetworks is based on equality comparison of hyperedges with learned data. Therefore, real-valued data need to be discretized by preprocessing before learning with hypernetworks. However, discretization causes inevitable information loss and possible decrease of accuracy in pattern classification. To overcome this weakness, we propose a novel feature-wise L1-distance based method for real-valued attributes in learning hypernetwork models in this study. We show that the proposed model improves the classification accuracy compared with conventional hypernetworks and it shows competitive performance over other machine learning methods.