• 논리연구
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• 제12권2호
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• pp.171-199
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• 2009

본 논문은 형식적인 조건을 통하여 뉴턴 물리학에서 아인슈타인 물리학으로의 이론변화의 연속성을 이해하려는 과학이론에 대한 진보적 견해를 비판적으로 고찰한다. '상응 원리'이나 '공변 원리'와 같은 형식적인 측면이 이 이론변화에서 중요한 정보를 담고 있는 것은 부정할 수 없지만, 이 형식적 속성은 뉴턴역학에서 아인슈타인의 특수 그리고 일반 상대성 이론으로의 발전에 대한 진화적 견해를 위한 본질적 요소를 제공하기에는 충분치 않다.

• International Journal of Control, Automation, and Systems
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• 제2권2호
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• pp.247-255
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• 2004

The majority of real-world problems encountered by engineers involve simultaneous optimization of competing objectives. In this case instead of single optima, there is a set of alternative trade-offs, generally known as Pareto-optimal solutions. The use of evolutionary algorithms Pareto GA, which was first introduced by Goldberg in 1989, has now become a sort of standard in solving Multiobjective Optimization Problems (MOPs). Though this approach was further developed leading to numerous applications, these applications are based on Pareto ranking and employ the use of the fitness sharing function to maintain diversity. Another scheme for solving MOPs has been presented by J. Nash to solve MOPs originated from Game Theory and Economics. Sefrioui introduced the Nash Genetic Algorithm in 1998. This approach combines genetic algorithms with Nash's idea. Another central achievement of Game Theory is the introduction of an Evolutionary Stable Strategy, introduced by Maynard Smith in 1982. In this paper, we will try to find ESS as a solution of MOPs using our game model based co-evolutionary algorithm. First, we will investigate the validity of our co-evolutionary approach to solve MOPs. That is, we will demonstrate how the evolutionary game can be embodied using co-evolutionary algorithms and also confirm whether it can reach the optimal equilibrium point of a MOP. Second, we will evaluate the effectiveness of our approach, comparing it with other methods through rigorous experiments on several MOPs.

• 한국조경학회지
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• 제28권2호
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• pp.49-60
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• 2000

The purpose of this study is to overview domestic and foreign trends in the study of the landscape evaluation through new framework of landscape evaluation studies. 108 studies on the landscape evaluation are summarized and categorized into theoretical studies, verification of theories, development of evaluation methods and applications in physical planning. Major theories in the landscape evaluation came from the psycho-physics, the evolutionary theory ann the cultural-learning theory, and were verified and applied into physical planning. Early experimental researches on landscape evaluation, based on psycho-physics, were focused on relatively simple responses to landscapes. But many studies have been gradually related to the evolutionary theory and the cultural learning theory, emphasizing biological and cultural effects on landscape evaluation. Especially, Appleton's Prospect-Refuge theory' and Kaplans' 'Information Processing model' have very strong influence in landscape evaluation. Relatively there have been many application researches in Korea, which tells there have been strong needs to solve pending practical problems caused by the rapid economic and social growth for several decades. Almost of applications in physical planning are focused on physical features of landscapes, but for more comprehensive landscape evaluation, many other factors such as cognitive and sociocultural variables should be integrated into the whole evaluation system. As a result of reviewing of landscape evaluation studies, I found the overall domestic and foreign trends and the necessity of more research on the applications in physical planning. Because this study mainly focused on academic researches, for more appropriate landscape evaluation and management there should be more practical researches including various approaches.

• 한국지능시스템학회논문지
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• 제12권6호
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• pp.491-496
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• 2002

다목적 함수 최적화 문제(Multi-objective Optimization Problems : MOPs)는 공학적인 문제를 풀고자 할 때 자주 접하게 되는 대표적인 문제 중 하나이다. 공학자들이 다루는 실세계 최적화 문제들은 몇 개의 경합하는 목적 함수(objective function) 들로 이루어진 문제일 경우가 많다. 본 논문에서는 다목적 함수 최적화 문제의 정의를 소개하고 이 문제를 풀기 위한 몇 가지 접근법을 소개한다. 먼저 서론에서는 파레토 최적해(Pareto optimal solution) 의 개념을 이용한 기존의 최적화 알고리즘과 이와는 달리 게임 이론(Game Theory) 으로부터 도출된 최적화 알고리즘인 내쉬 유전자 알고리즘(Nash Genetic Algorithm Nash GA) 그리고 본 논문에서 제안하는 공진화 알고리즘의 기반이 되는 진화적 안정 전략 (Evolutionary Stable Strategy : ESS) 의 이론적 배경을 소개한다. 또 본론에서는 다목적 함수 최적화 문제와 파레토 최적 해의 정의를 소개하고 다목적 함수 최적화 문제를 풀기 위하여 유전자 알고리즘을 진화적 게임 이론(Evolutionary Game Theory : EGT) 에 적용시킨 내쉬 유전자 알고리즘과 본 논문에서 새로이 제안하는 공진화 알고리즘의 구조를 설명하고 이 두 가지 알고리즘을 대표적인 다목적 함수 최적화 문제에 적용하고 결과를 비교 검토함으로써 진화적 게임 이론의 두 가지 아이디어 내쉬의 균형(Equilibrium) 과 진화적 안정전략 에 기반한 최적화 알고리즘들이 다목적 함수 문제의 최적해 를 탐색할 수 있음을 확인한다.

• 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.

• 대한기계학회논문집A
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• 제26권6호
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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.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권12호
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• pp.3133-3151
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• 2012

The optimal price strategy selection of two bounded rational cognitive mobile virtual network operators (MVNOs) in a duopoly spectrum sharing market is investigated. The bounded rational operators dynamically compete to sell the leased spectrum to secondary users in order to maximize their profits. Meanwhile, the secondary users' heterogeneous preferences to rate and price are taken into consideration. The evolutionary game theory (EGT) is employed to model the dynamic price strategy selection of the MVNOs taking into account the response of the secondary users. The behavior dynamics and the evolutionary stable strategy (ESS) of the operators are derived via replicated dynamics. Furthermore, a reward and punishment mechanism is developed to optimize the performance of the operators. Numerical results show that the proposed evolutionary algorithm is convergent to the ESS, and the incentive mechanism increases the profits of the operators. It may provide some insight about the optimal price strategy selection for MVNOs in the next generation cognitive wireless networks.

• STI Policy Review
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• 제3권1호
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• pp.1-45
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• 2012

This paper reviews the emerging literature on innovation policy from a practitioner's perspective, reviews the policy implications of an evolutionary perspective in economics, and identifies newly emerging areas of innovation policy analysis. We show that an innovation system is a dominant policy discourse, that there are limits of policy implications from an evolutionary perspective, and that there is a need for explicitly policy motivated analysis grounded in a broad evolutionary perspective. Innovation policy studies are an area in pre-paradigmatic stages whose dynamism originates from the interaction of theory, policy, and policy learning. The limited usefulness of policy implications derived directly from either conceptual or empirical analysis requires more explicitly policy focused research.

• 한국게임학회 논문지
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• 제11권2호
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• pp.57-68
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• 2011

본 연구는 새롭게 탄생한 온라인 게임 기업이 시장 성장주기(도입기-성장기-성숙기)에 따라 어떻게 다원화 전략을 행하여 왔는가를 자원기반이론과 진화이론의 관점에서 실증적 사례 분석을 통해 그 역사적 진행 과정을 체계적으로 분석하였다. 초기 온라인 게임 기업들은 진입 조건(장르, 기술력, 이용자특성)에 따라 다른 전략(기술역량기반, 서비스역량기반)을 통해 성장하였다. 이후 성장기에 이들 기업들은 제품 다원화를 위해 자원기반(기술기반전략, 서비스기반전략)에 따라 경로의존적 제휴 전략(보완적, 대체적 제휴)을 수행하여왔다. 그러나 성숙기에 이들 기업들은 기존 경로의존적 전략을 뛰어넘어 이용가능한 모든 자원 역량을 동원하는 통합 전략을 자연적으로 선택함으로서 시장 성장주기에 탄력적으로 적응하였다. 이러한 분석 결과는 진화이론과 자원기반이론을 복합적으로 적용하여 새롭게 탄생한 산업에서 시장의 단계별 성장주기에 따라 온라인 게임 기업의 제휴 전략 패턴이 어떻게 자기조직화 하고 있는지 분석함으로써 새로운 산업적, 정책적, 이론적 모델이 요구되고 있음을 제시하고 있다.