• Knowledge Management Research
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• v.19 no.3
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• pp.1-29
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• 2018

This study focuses on the mediating role of creativity on the knowledge management process. To confirm focal hypotheses, we conducted survey on 538 employees of foreign subsidiaries of global enterprises. Main findings are: first, creativity turned out to positively mediate the effect of knowledge transfer on the firm's performance; second, the mediating role of creativity differs depending on the domain of acquiring knowledge such as exploitation and exploration. This research found that the influence of exploitative knowledge on knowledge creation and innovative performance was stronger. As such, among tension view and foundational view, which are two competing views on the relationship between knowledge and creativity, the latter is more soundly supported.

• Advances in Computational Design
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• v.1 no.3
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• pp.275-296
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• 2016

This article presents an adaptive directional differential evolution (ADDE) algorithm and its application in solving discrete sizing truss optimization problems. The algorithm is featured by a new self-adaptation approach and a simple directional strategy. In the adaptation approach, the mutation operator is adjusted in accordance with the change of population diversity, which can well balance between global exploration and local exploitation as well as locate the promising solutions. The directional strategy is based on the order relation between two difference solutions chosen for mutation and can bias the search direction for increasing the possibility of finding improved solutions. In addition, a new scaling factor is introduced as a vector of uniform random variables to maintain the diversity without crossover operation. Numerical results show that the optimal solutions of ADDE are as good as or better than those from some modern metaheuristics in the literature, while ADDE often uses fewer structural analyses.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.6
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• pp.642-647
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• 2014

In this paper, a hybrid genetic algorithm (HGA) approach is proposed for an n-job, m-machine flow shop scheduling problem with limited capacity buffers with blocking in which the objective is to minimize the total stretch. The stretch of a job is the ratio of the amount of time the job spent before its completion to its processing time. HGA adopts the idea of seed selection and development in order to improve the exploitation and exploration power of genetic algorithms (GAs). Extensive computational experiments have been conducted to compare the performance of HGA with that of GA.

• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.193-217
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• 2001

Despite of the rich literature in discriminant analysis, this complicated subject remains much to be explored. In this article, we study the theoretical foundation that supports Fisher's linear discriminant analysis (LDA) by setting up the classification problem under the dimension reduction framework as in Li(1991) for introducing sliced inverse regression(SIR). Through the connection between SIR and LDA, our theory helps identify sources of strength and weakness in using CRIMCOORDS(Gnanadesikan 1977) as a graphical tool for displaying group separation patterns. This connection also leads to several ways of generalizing LDA for better exploration and exploitation of nonlinear data patterns.

• Proceedings of the Safety Management and Science Conference
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• 2010.04a
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• pp.401-414
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• 2010

Global financial crisis at 2008 caused a great change of the company itself and environment as hit product, new creative business model. But it is not easy program. Ambidextrous organization excel at exploiting existing products to enable incremental innovation and at exploring new opportunities fo foster more radical innovation. In this paper are presented the improved procedures of new business model development using 6 sigma methodology. To inspired about new business ideas we use exploration method. To eliminate project's waste we use exploitation method such as 6sigma methodology. The main object of this study is to introduce an empirical study on DIDOV.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.106-108
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• 2006

강화 학습은 누적 보상 값을 최대화할 수 있는 행동 선택 전략을 학습하는 온라인 학습의 한 형태이다. 효과적인 강화학습을 위해 학습 에이전트가 매 순간 고민해야 하는 문제가 탐험(exploitation)과 탐색(exploration)의 문제이다. 경험과 학습이 충분치 않은 상태의 에이전트는 어느 정도의 보상 값을 보장하는 과거에 경험한 행동을 선택하느냐 아니면 보상 값을 예측할 수 없는 새로운 행동을 시도해봄으로써 학습의 폭을 넓힐 것이냐를 고민하게 된다. 특히 단일 에이전트에 비해 상태공간과 행동공간이 더욱 커지는 다중 에이전트 시스템의 경우, 효과적인 강화학습을 위해서는 상태 공간 축소방법과 더불어 탐색의 기회가 많은 행동 선택 전략이 마련되어야 한다. 본 논문에서는 로봇축구 Keepaway를 위한 효율적인 다중 에이전트 강화학습 방법을 설명한다. 이 방법의 특징은 상태 공간 축소를 위해 함수근사방법의 하나인 타일 코딩을 적용하였고, 다양한 행동 선택을 위해 룰렛 휠 선택 전략을 적용한 것이다. 본 논문에서는 이 방법의 효과를 입증하기 위한 실험결과를 소개한다.

• The Korean Journal of Petroleum Geology
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• v.9 no.1_2 s.10
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• pp.24-39
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• 2001

Prospecting for energy and mineral resources is essential kind of public fundamentals that manage the nation's economy. Most explorations in the past were concentrated in the simple structural traps in relatively shallow depth. Due to their vast exploitation, recent history has shown that the emphasis in explorations has steadily shifted toward the subtle stratigraphic traps in deeper level. Increasing exploration for the subtle stratigraphic traps in deeper level requires precise correlation and assessment of deeply buried strata in the basin. However, the descriptive stratigraphic principles used for evaluation of the simple structural traps are limited to delineate the subtle stratigraphic traps in deeper depth. As this occurs, it is imperative to establish a new stratigrtaphic paradigm that allows a more sophisticated understanding on the basin stratigraphy. This study provides an exemplary application of integrated stratigraphic approach to defining basin stratigraphy of the Middle Ordovician Taebacksan Basin and the Cretaceous South Yellow Sea Basin, Korea. The integrated stratigraphic approach gives much better insight to unravel the stratigraphic response to tectonic evolution of the basins, which can be utilized for enhancing the efficiency of resources exploration and development in the basins. Thus, the integrated stratigraphic approach should be considered as a new stratigraphic norm that can improve the probability of success in any type of resources exploration and development project.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.281-293
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• 2022

The necessity for introducing artificial intelligence(AI) into the public sector to form an intelligent government has been emerging. This study set 'Organizational Agility', 'Exploitation & Exploration Learning', and 'E-government Capability' as independent variables for the introduction of AI in central government. Dependent variables were set on whether AI was adopted in the central government organization 'Bu(mainly conducts policy planning)', and 'Cheong(mainly performs policy execution)'. Logistic regression analysis was performed on each of the two models. As a result, it was derived that ministry Bu adopted AI as organizational agility increased, and ministry Chung adopted AI as e-government capability increased. Particularly, it was identified that the effect of exploitation learning for Cheong organizations offset the influence of AI introduction according to e-government capabilities, while exploratory organizational learning facilitated the AI introduction. This study is meaningful for suggesting a strategy for adopting AI in government.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.1
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• pp.13-27
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• 2016

Companies strive to have the ability to flexibly respond to environmental changes in modern society with its rapidly changing business environment. That is, companies try to achieve the corporate performance by using a variety of strategies since companies that don't go along with changes in industry are likely to fall behind. Also, the corporate performance is a key element in national competitiveness, and government is willing to support companies to maximize their performance in various ways. This study examines whether there is a difference between corporate strategies and government policies according to the retention and the type of certification of innovation. The company's strategy configuration effort is largely divided into exploration and exploitation of external knowledge, while the government's policy is divided into direct support, indirect support, and financial support. The corporate performance is analyzed using technological performance; innovative perspective and the sales; and the actual corporate performance as proxy variables. As a result, the variable affecting the performance differs according to the retention of certification of innovation. The variable affecting the corporate performance differs according to the type of certification of innovation as well. Therefore, it was found that companies can achieve the corporate performance by considering the situation at hand and the differentiated action strategies depending on the type of certification of innovation.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.669-680
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• 2001

Genetic Algorithms are optimization algorithm that mimics biological evolution to solve optimization problems. Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex fitness landscapes. Hybrid genetic algorithm that is combined with local search called learning can sustain the balance between exploration and exploitation. The genetic traits that each individual in the population learns through evolution are transferred back to the next generation, and when this learning is combined with genetic algorithm we can expect the improvement of the search speed. This paper proposes a genetic algorithm based Cellular Learning with accelerated learning capability for function optimization. Proposed Cellular Learning strategy is based on periodic and convergent behaviors in cellular automata, and on the theory of transmitting to offspring the knowledge and experience that organisms acquire in their lifetime. We compared the search efficiency of Cellular Learning strategy with those of Lamarckian and Baldwin Effect in hybrid genetic algorithm. We showed that the local improvement by cellular learning could enhance the global performance higher by evaluating their performance through the experiment of various test bed functions and also showed that proposed learning strategy could find out the better global optima than conventional method.