• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.549-557
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• 2014

Recently, analysis of bargaining game using evolutionary computation is essential issues in field of game theory. In this paper, we observe a bargaining game using co-evolution between two heterogenous artificial agents. In oder to model two artificial agents, we use a particle swarm optimization and a differential evolution. We investigate algorithm parameters for the best performance and observe that which strategy is better in the bargaining game under the co-evolution between two heterogenous artificial agents. Experimental simulation results show that particle swarm optimization outperforms differential evolution in the bargaining game.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.268-277
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• 2020

This study emphasizes that the survival strategy of universities should be a co-evolution strategy based on ecological thinking. Therefore, the purpose of the research is to present a theoretical framework for dividing the university innovation ecosystem into four stages and building a co-evolution strategy for each step, as universities play a prominent role in regional innovation ecosystems. Thus, our research method focused on literature research, and the theoretical framework for the university innovation ecosystem used Moore's Enterprise Ecosystem Model (1996). The university's ecological innovation strategy is divided into four stages of development, and a step-by-step co-evolution strategy is presented. Findings are summarized as follows. The pioneering stage involves the creation of values of the university-led innovation ecosystem. The expansion stage focuses on the establishment of critical mass. The authority stage covers maintaining authority and bargaining power. The renewal stage features continuous performance improvement. In particular, this theoretical model of the university-regional innovation ecosystem is meaningful in that it provides a theoretical basis for enhancing the effectiveness of government financial support projects, and for individual universities, it provides a framework for strategies suitable for their ecosystem building process.

• Journal of Technology Innovation
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• v.28 no.4
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• pp.63-93
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• 2020

We suggest a new way of specifying the co-evolution of product and process technologies, and integrating it into one of the well-received technology intelligence tools - a technology radar. Cross impact analysis enables us to identify the core technologies of product-process co-evolution. Combining expert judgment with its results, we can clarify the technological co-evolution trajectory with mainstream as well as emerging core technologies. Reflecting these in the assessment process of a technology radar, we could improve reliance of the technology assessment process and technology portfolio. From the academic perspective, our research provides a point where the co-evolution theory encouners technology intelligence methods. Practically, strategic capability of future-preparedness and strategic management could improve by adopting our method based on our example of co-evolution of semiconductor product and process technologies.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.395-398
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• 2002

본 논문에서는 ‘다목적 함수 최적화 문제(Multi-objective Optimization Problem MOP)’를 풀기 위하여 유전자 알고리즘을 진화적 게임 이론 적용시킨 ‘내쉬 유전자 알고리즘(Nash GA)’과 본 논문에서 새로이 제안하는 공진화 알고리즘의 구조를 설명하고 이 두 알고리즘의 결과를 시뮬레이션을 통하여 비교 검토함으로써 ‘진화적 게임 이론(Evolutionary Game Theory : EGT)’의 두 가지 아이디어 -‘내쉬의 균형(Equilibrium)’과 ‘진화적 안정전략(Evolutionary Stable Strategy . ESS)’-에 기반한 최적화 알고리즘들이 다목적 함수 문제의 최적해를 탐색할 수 있음을 확인한다.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2010.05a
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• pp.124-139
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• 2010

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.5
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• pp.185-196
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• 2011

In order to draw some strategic implications for the sustainable growth of emerging technologies this paper attempts to dynamics underlying the 'hype cycle' ever occurring in course of coevolution of technology and society. Particularly, a series of basic questions in the context of sustainability are explored to answer by simulating the hype system structure: What makes hype cycle occur? how to enhance the tapering level at the final stage of coevolution? what are the key policy leverages and when is the right time for the policy intervention? This study perhaps give some insights not necessarily to the academics but also to the practitioners and policy makers.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.253-261
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• 2004

Game theory is mathematical analysis developed to study involved in making decisions. In 1928, Von Neumann proved that every two-person, zero-sum game with finitely many pure strategies for each player is deterministic. As well, in the early 50's, Nash presented another concept as the basis for a generalization of Von Neumann's theorem. Another central achievement of game theory is the introduction of evolutionary game theory, by which agents can play optimal strategies in the absence of rationality. Not the rationality but through the process of Darwinian selection, a population of agents can evolve to an Evolutionary Stable Strategy (ESS) introduced by Maynard Smith. Keeping pace with these game theoretical studies, the first computer simulation of co-evolution was tried out by Hillis in 1991. Moreover, Kauffman proposed NK model to analyze co-evolutionary dynamics between different species. He showed how co-evolutionary phenomenon reaches static states and that these states are Nash equilibrium or ESS introduced in game theory. Since the studies about co-evolutionary phenomenon were started, however many other researchers have developed co-evolutionary algorithms, in this paper we propose Game theory based Co-Evolutionary Algorithm (GCEA) and confirm that this algorithm can be a solution of evolutionary problems by searching the ESS.To evaluate newly designed GCEA approach, we solve several test Multi-objective Optimization Problems (MOPs). From the results of these evaluations, we confirm that evolutionary game can be embodied by co-evolutionary algorithm and analyze optimization performance of GCEA by comparing experimental results using GCEA with the results using other evolutionary optimization algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.559-560
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• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, applying differential evolution to solve large-scale optimization problems dramatically degrades performance and exponentially increases runtime. Therefore, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.699-704
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• 2000

The present paper deals with a multiobjective optimization method based on the co-evolutionary genetic strategy. The co-evolutionary strategy carries out the multiobjective optimization in such way that it optimizes individual objective function as compared with each generation's value while there are more than two genetic evolutions at the same time. In this study, the designs that are out of the given constraint map compared with other objective function value are excepted by the penalty. The proposed multiobjective genetic algorithms are distinguished from other optimization methods because it seeks for the optimized value through the simultaneous search without the help of the single-objective values which have to be obtained in advance of the multiobjective designs. The proposed strategy easily applied to well-developed genetic algorithms since it doesn't need any further formulation for the multiobjective optimization. The paper describes the co-evolutionary strategy and compares design results on the simple structural optimization problem.

• Journal of Cadastre & Land InformatiX
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• v.44 no.1
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• pp.1-15
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• 2014

This paper, at first, intends to develop a conceptual model of spatial information eco-system based on the related literature reviews. The basic requirements in constructing the model are also specified. Next, the functional roles and interrelationships among the actors constituting the eco-system are analyzed to investigate the major reasons of inefficient and unsmooth flows of value-added process of Korean spatial information industry. Especially, the Korea Cadastral Survery Corp.(KCSC), which has dual organizational characteristics of public and private entity, is analyzed to be positioned as the most dominant actors in the eco-system. However, the KCSC needs to be changed and challenged to re-establish the missions and institutional roles for upcoming network societies. Here, we proposed 4 future-oriented development strategies and action plans to promote the Korean spatial information industry and to activate the eco-system.