• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2115-2123
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• 2006

The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel. The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies.

• Korean System Dynamics Review
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• v.7 no.2
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• pp.5-20
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• 2006

In the advent of ubiquitous information technology (u-IT) as a new emerging horizon of information society, inflated expectations regarding u-IT are growing very fast and higher than those made in the past, which would perhaps result in serious bust after boom and incur tremendous amount of social costs. This paper thus investigates a dynamic mechanism underlying the coevolution between information technology and society by applying systems thinking, particularly, with a focus on the typical phenomenon, 'hype curve' which shows how new technologies initially grow too fast for their own good, crashing from a peak of inflated expectations into a trough of disillusionment before stabilizing on a plateau of productivity. Three basic questions are explored to answer by investigating the mechanisms underlying the 'boom-bust' phenomenon: First, why hype curve appears in the process of technology and society coevolution. Second, how to enhance the stabilization level. Third, when is the right time for the policy intervention.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.3
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• pp.239-249
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• 2001

Competitive coevolution models, often called host-parasite models, are searching models that imitate the biological coevolution that is a series of reciprocal changes in two competing species. The models are known to be an effective method of solving complex and dynamic problems such as game problems, neural network design problems and constraint satisfaction problems. However, previous models consider only ectoparasites that live on the outside of the host when designing the models, not considering endoparasites that live on the inside of the host. This has a limitation to exploiting some information. In this paper, we develop an artificial adaptation model simulating the process in which hosts coevolve with both ectoparasites and endoparasites. In the model, the endoparasites play important roles as follows. By means of them, we can keep the history on results of previous competition between hosts and parasites, and use endogeneous fitness, not exogeneous. Extensive experiments are carried out to show the coevolution phenomenon and to verify the performance of the proposed model. Nim game problems and neural network problems are used as test-bed problems. The results are reported in this paper.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.155-160
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• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.690-693
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• 2003

The purpose of two player game is that a player beats an enemy. In order to win to various enemies, a learning of various strategies is indispensable. However, the optimal action to overcome the enemies will change when the game done over and again because the enemy's actions also change dynamically. Sol it is din-cult that the player aquires the optimal action and that the specific player keeps winning to various enemies. Species who have a competition relation and affect other's existence is called a coevolution. Coevolution has recently attracred considerable interest in the community of Artificial Life and Evolutionary Computation(1). In this paper, we apply Classifier System for agent team to two player game. A reward and a penalty are given to the used rules when the agent achieve specific action in the game and each team's rulebase are evaluated based on the ranking in the league. We show that all teams can acquire the optimal actions by coevolution.

• Korean System Dynamics Review
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• v.9 no.1
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• pp.171-197
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• 2008

By applying Systems Simulation technique, this paper aims to investigates the dynamics underlying the coevolution of IT(information technology) and the society. Particularly, a series of basic questions are explored to answer by developing a simulation model for the mechanisms underlying the 'hype curve' ever occurring in the course of technology diffusion into society: First, why hype curve appears in the process of technology and society coevolution. Second, how to enhance the tapering level at the final stage of coevolution. Third, what are the key policy leverages and when is the right time for the policy intervention. As now, inflated expectations regarding ubiquitous information technology (u-IT) are growing very fast and higher than those for the previous technologies, which would result in overshoot followed by collapse of visibility and thus incur tremendous amount of social costs. In this regard implications drawn from this study perhaps give some insights not necessarily to the academics but also to the practitioners and policy makers facing the advent of u-IT as a new emerging horizon of information society.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.2
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• pp.77-95
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• 2004

This paper deals with the process planning of flexible manufacturing systems (FMS) with various flexibilities and multiple objectives. The consideration of the manufacturing flexibility is crucial for the efficient utilization of FMS. The machine, tool, sequence, and process flexibilities are considered In this research. The flexibilities cause to increase the Problem complexity. To solve the process planning problem, an this paper an evolutionary algorithm is used as a methodology. The algorithm is named multiobjective competitive evolutionary algorithm (MOCEA), which is developed in this research. The feature of MOCEA is the incorporation of competitive coevolution in the existing multiobjective evolutionary algorithm. In MOCEA competitive coevolution plays a role to encourage population diversity. This results in the improvement of solution quality and, that is, leads to find diverse and good solutions. Good solutions means near or true Pareto optimal solutions. To verify the Performance of MOCEA, the extensive experiments are performed with various test-bed problems that have distinct levels of variations in the four kinds of flexibilities. The experiments reveal that MOCEA is a promising approach to the multiobjective process planning of FMS.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.17-18
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• 2014

• 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