• 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.

• IE interfaces
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• v.18 no.4
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• pp.465-476
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• 2005

Evolutionary operation is a useful tool for improving full-scale manufacturing process by systematically changing the levels of the process variables without jeopardizing the product. This paper presents a system design for the evolutionary operation software called 'evolutionary optimizer'. Evolutionary optimizer consists of four modules: factorial design, many variables, mixture, and mean/dispersion. Context diagram, data flow diagram and entity-relationship modelling are used to systematically design the evolutionary optimizer system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.185-190
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• 2002

In distributed autonomous robotic systems (DARS), each robot must behave by itself according to its states ad environments, and if necessary, must cooperate with other robots in order to carry out their given tasks. Its most significant merit is that they determine their behavior independently, and cooperate with other robots in order to perform the given tasks. Especially, in DARS, it is essential for each robot to have evolution ability in order to increase the performance of system. In this paper, a schema co-evolutionary algorithm is proposed for the evolution of collective autonomous mobile robots. Each robot exchanges the information, chromosome used in this algorithm, through communication with other robots. Each robot diffuses its chromosome to two or more robots, receives other robot's chromosome and creates new species. Therefore if one robot receives another robot's chromosome, the robot creates new chromosome. We verify the effectiveness of the proposed algorithm by applying it to cooperative search problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.354-360
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• 1998

In general, it is very difficult to find optimal fuzzy rules by experience when a system is dynamical and/or complex. Futhermore proper fuzzy partitioning is not deterministic and there is no unique solution. Therefore we propose a new design method of an optimal fuzzy logic controller, that is a co-evolutionary genetic algorithm finding optimal fuzzy rule and proper membership functions at the same time. We formalize the relation between fuzzy rules and membership functions in terms of fitness. We review the typical approaching methods to co-evolutionary genetic algorithms , and then classify them by fitness relation matrix. Applications of the proposed method to a path planning problem of autonomous mobile robots when moving objects exist are presented to demonstrate the performance and effectiveness of the method.

• 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.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.101-110
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• 2004

In dynamic systems, such as social and economic systems, complex interactions emerge among its members. In that case, their behaviors become adaptive according to Changing environment. In many cases, an individual's behaviors can be modeled by a stimulus-response system in a dynamic environment. In this paper, we use the Iterated Prisoner's Dilemma (IPD) game, which is simple yet capable of dealing with complex problems, to model the dynamic systems. We propose strategic coalition consisting of many agents and simulate their emergence in a co-evolutionary learning environment. Also we introduce the concept of confidence for agents in a coalition and show how such confidences help to improve the generalization ability of the whole coalition. Experimental results are presented to demonstrate that co-evolutionary learning with coalitions and confidence allows better performing strategies that generalize well.

• Genomics & Informatics
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• v.18 no.3
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• pp.30.1-30.7
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• 2020

The novel coronavirus pandemic that has originated from China and spread throughout the world in three months. Genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) predecessor, severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) play an important role in understanding the concept of genetic variation. In this paper, the genomic data accessed from National Center for Biotechnology Information (NCBI) through Molecular Evolutionary Genetic Analysis (MEGA) for statistical analysis. Firstly, the Bayesian information criterion (BIC) and Akaike information criterion (AICc) are used to evaluate the best substitution pattern. Secondly, the maximum likelihood method used to estimate of transition/transversions (R) through Kimura-2, Tamura-3, Hasegawa-Kishino-Yano, and Tamura-Nei nucleotide substitutions model. Thirdly and finally nucleotide frequencies computed based on genomic data of NCBI. The results indicate that general times reversible model has the lowest BIC and AICc score 347,394 and 347,287, respectively. The transition/transversions bias for nucleotide substitutions models varies from 0.56 to 0.59 in MEGA output. The average nitrogenous bases frequency of U, C, A, and G are 31.74, 19.48, 28.04, and 20.74, respectively in percentages. Overall the genomic data analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV highlights the close genetic relationship.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.82-85
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• 2003

In this paper, in order to deduce the deep structure of a set of fuzzy rules from the surface structure, we use co-evolutionary algorithm based on modified Nash GA. This algorithm coevolves membership functions in antecedents and parameters in consequents of fuzzy rules. We demonstrate this co-evolutionary algorithm and apply to the mobile robot control. From the result of simulation, we compare modified Nash GA with the other co-evolution algorithms and verify the efficacy of this algorithm through application to fuzzy systems.

• Land and Housing Review
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• v.11 no.1
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• pp.39-48
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• 2020

Main research question of this study is about whether gated community (GC) as private urban governance gets along with local public goods by locating near to them. We examine this question through testing the Tiebout hypothesis from case study of Seoul, capital city of South Korea, in which GCs are so common to test the assumption empirically. For this, we examine the meaning of GC in 3 Es viewpoints; conceptualize the framework of Tieboutian co-evolution of GC and local public goods by hedonic price modeling. As a result, possibilities are found that GCs are to be seen from different point of view, viz. co-evolutionary mechanism between private and public governance; GCs effectively capture and represent the demand of residents for local public goods through voting by their collective locational choice. It allows us different kind of approach to investigate APTs as a co-evolutionary form of private and public urban order rather than seeing them only as a tool of speculative investment, particularly in rapidly urbanizing countries like Korea.

• 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.