• 한국지능시스템학회논문지
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• 제14권3호
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• pp.253-261
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• 2004

게임 이론은 의사 결정 문제와 관련 된 연구와 함께 정립 된 수학적 분석법으로써 1928년 Von Neumann이 유한개의 순수전략이 존재하는 2인 영합게임은 결정적(deterministic)이라는 것을 증명함으로써 수학적 기반을 정립하였고 50년대 초, Nash는 Von Neumann의 이론을 일반화하는 개념을 제안함으로써 현대적 게임이론의 장을 열었다. 이후 진화 생물학 연구자들에 의해 고전적인 게임 이론의 가정에 해당하는 참가자들의 합리성(rationality) 대신 다윈 선택(Darwinian selection)에 의해 게임의 해를 탐색하는 것이 가능하다는 것이 밝혀지게 되었고 진화 생물학자 Maynard Smith에 의해 진화적 안정 전략(Evolutionary Stable Strategy: ESS)의 개념이 정립되면서 현대적 게임 이론으로써 진화적 게임 이론이 체계화 되었다. 한편 이와 같은 진화적 게임 이론에 관한 연구와 함께 생태계의 공진화를 이용한 컴퓨터 시뮬레이션이 1991년 Hillis에 의해 처음으로 시도되었으며 Kauffman은 다른 종들 간의 공진화적 동역학(dynamics)을 분석하기 위한 NK 모델을 제안하였다. Kauffman은 이 모델을 이용하여 공진화 현상이 어떻게 정적 상태(static state)에 이르며 이 상태들은 게임 이론에서 소개되어진 내쉬 균형이나 ESS에 해당한다는 것을 보여주었다. 이후, 몇몇 연구자들 게임 이론과 진화 알고리즘에 기반한 연산 모델들을 제시해 왔으나 실용적인 문제의 적용에 대한 연구는 아직 미흡한 편이다. 이에 본 논문에서는 게임 이론에 기반 한 공진화 알고리즘을(Game theory based Co-Evolutionary Algorithm: GCEA) 제안하고 이 알고리즘을 이용하여 공진화적인 문제들을 효과적으로 해결할 수 있음을 확인하는 것을 목표로 한다.

• 제어로봇시스템학회논문지
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• 제5권3호
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• pp.281-287
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• 1999

In this paper, a design method based on evolutionary programming for feedforward neural networks which have a single hidden layer is presented. By using an evolutionary programming, the network parameters such as the network structure, weight, slope of sigmoid functions and bias of nodes can be acquired simultaneously. To check the effectiveness of the suggested method, two numerical examples are examined. The performance of the identified network is demonstrated.

• East Asian mathematical journal
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• 제16권2호
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• pp.239-250
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• 2000

We study some properties of N-dichotomy for evolutionary process and generalize the theory of the uniform N-equistability using these properties.

• 한국과학교육학회지
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• 제31권3호
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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.

• The Plant Pathology Journal
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• 제35권5호
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• pp.498-507
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• 2019

Odontoglossum ringspot virus (ORSV) is a member of the genus Tobamovirus. It is one of the most prevalent viruses infecting orchids worldwide. Earlier studies reported the genetic variability of ORSV isolates from Korea and China. However, the evolutionary rate, timescale, and phylogeographical analyses of ORSV were unclear. Twenty-one coat protein (CP) gene sequences of ORSV were determined in this study, and used them together with 145 CP sequences obtained from GenBank to infer the genetic diversities, evolutionary rate, timescale and migration of ORSV populations. Evolutionary rate of ORSV populations was $1.25{\times}10^{-3}nucleotides/site/y$. The most recent common ancestors came from 30 year ago (95% confidence intervals, 26-40). Based on CP gene, ORSV migrated from mainland China and South Korea to Taiwan island, Germany, Australia, Singapore, and Indonesia, and it also circulated within east Asia. Our study is the first attempt to evaluate the evolutionary rates, timescales and migration dynamics of ORSV.

• 생물정신의학
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• 제29권1호
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• pp.1-8
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• 2022

Objectives In regard to the social competition hypothesis, depression is viewed as an involuntary defeat strategy. A previous study has demonstrated that adaptation in microenvironments can result in a wide range of behavioural patterns including defense activation disorders. Using a simulation model with evolutionary ecological agents, we explore how the fitness of various defence activation traits has changed over time in different environments with high and low social mobility. Methods The Evolutionary Ecological Model of Defence Activation Disorder, which is based on the Marginal Value Theorem, was used to examine changes in relative fitness for individuals with defensive activation disorders after adjusting for social mobility. Results Our study examined the effects of social mobility on fitness by varying the d-values, a measure of depression in the model. With a decline in social mobility, the level of fitness of individuals with high levels of defense activation decreased. We gained insight into the evolutionary influence of varying levels of social mobility on individuals' degrees of depression. In the context of a highly stratified society, the results support a mismatch hypothesis which states that high levels of defence are detrimental. Conclusions Despite the fact that niche specialization in habitats composed of multiple microenvironments can result in diverse levels of defensive activation being evolutionary strategies for stability, decreased social mobility may lead to a decrease in fitness of individuals with highly activated defence modules. There may be a reason behind the epidemic of depression in modern society.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

• 한국조리학회지
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• 제24권1호
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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.

• 한국농공학회:학술대회논문집
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• 한국농공학회 1999년도 Proceedings of the 1999 Annual Conference The Korean Society of Agricutural Engineers
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• pp.219-224
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• 1999

In this study New design automation method of R/C structure based on the finite element method and the analogy evolution ary procedure was developed . This system is the efficient tool to support Reinforcement Arrangement of R/C structure. The anology evolutionary procecure is similar to the evolutionary procedure proposed by Xie and Steven.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.