• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.463-474
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• 2004

Game theory is a method of mathematical analysis developed to study the decision making process. In 1928, Von Neumann mathematically proved that every two-person, zero-sum game with many pure finite strategies for each player is deterministic. 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. Through the process of Darwinian selection, a population of agents can evolve to an Evolutionary Stable Strategy (ESS) as introduced by Maynard Smith in 1982. Keeping pace with these game theoretical studies, the first computer simulation of coevolution was tried out by Hillis. Moreover, Kauffman proposed the NK model to analyze coevolutionary dynamics between different species. He showed how coevolutionary phenomenon reaches static states and that these states are either Nash equilibrium or ESS in game theory. Since studies concerning coevolutionary phenomenon were initiated, there have been numerous other researchers who have developed coevolutionary algorithms. In this paper we propose a new coevolutionary algorithm named Game theory based Coevolutionary Algorithm (GCEA) and we confirm that this algorithm can be a solution of evolutionary problems by searching the ESS. To evaluate this newly designed approach, we solve several test Multiobjective Optimization Problems (MOPs). From the results of these evaluations, we confirm that evolutionary game can be embodied by the coevolutionary algorithm and analyze the optimization performance of our algorithm by comparing the performance of our algorithm with that of other evolutionary optimization algorithms.

• Structural Engineering and Mechanics
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• v.7 no.4
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• pp.401-412
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• 1999

The purpose of this paper is to show how the Evolutionary Structural Optimization (ESO) algorithm developed by Xie and Steven can be extended to optimal design problems of thin shells subjected to thermal loading. This extension simply incorporates an evolutionary iterative process of thermoelastic thin shell finite element analysis. During the evolution process, lowly stressed material is gradually eliminated from the structure. This paper presents a number of examples to demonstrate the capabilities of the ESO algorithm for solving topology optimization and thickness distribution problems of thermoelastic thin shells.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.92-95
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• 2004

A mixture experiment is a special type of response surface experiment in which the factors are the ingredients or components of a mixture, and the response is a function of the proportions of each ingredient. Evolutionary operation is useful to improve on-line full-scale manufacturing processes by systematically changing the levels of the process variables without jeopardizing the product. This paper presents an evolutionary operation procedure for large-scale mixture production processes based on simplex search procedure, which can be beneficial to practitioners who should improve on-line mixture process quality while meeting the production schedule of the mixture product.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1124-1129
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• 2003

In mechanical design, design process is mainly composed of design, explanation and evaluation. In this paper, Using Genetic Algorithms (GA), Evolutionary computation is introduced as new design process. This method promote the efficiency and power of design. Due to the known characteristics of the stage, the approach basically involves a synthetic design method with the composition of building blocks representing the elements of mechanical objects. In order for the building blocks to be more suitable for representation and evolution of mechanical structures, Elementary Cell Blocks (ECBs) are introduced as new building blocks. In this paper, we have demonstrated the implementation of the approach with the design of gear systems.

• Journal of the Korean Society of Systems Engineering
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• v.3 no.1
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• pp.9-15
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• 2007

An EA(Evolutionary Acquisition) strategy is based on the systems engineering. It is a preferred approach to provide operationally useful capabilities to the warfighter much more quickly than single-step to full capability strategy. Recently, DoD is trying to apply the acquisition process based on the systems engineering. In spite of these trends, efforts of domestic defense acquisition society to this strategy are insufficient. Although an EA strategy has many benefits, there are many constraints to apply it. This study analyzes these constraints and presents applications of the EA strategy to defense R&D programs.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.4
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• pp.701-710
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• 2002

The framework, described in this paper, involves artificial evolutionary systems that re -produce aimed solutions through a simulated Darwinian evolution process. Through this process the framework designs structures of machines innovatively and emergently especially in the stages of conceptual and basic design. Since the framework simulates the evolution of nature, it inevitably involves processes that converse the natural evolution to the artificial evolution. For the conversion, based on several methods as the building block modeling, Artificial Life, evolutionary computation and the law of natural selection, we propose a series of processes that consists of modeling, evaluation, selection, evolution etc. We have demonstrated the implementation of the framework with the design of multi-step gear systems.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.6
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• pp.590-597
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• 2009

The final quality of the workpiece is affected by the grinding process that has been conducted in final manufacturing stage. However the quality-satisfaction of ground workpiece depends on the skill of an expert in this process. Therefore, the process models of grinding have been developed to predict the states according to grinding process. In this paper, in order to find the optimized grinding condition to reduce the manufacturing expense and to meet requirements of ground workpiece optimization algorithm using E.S.(Evolutionary Strategy) is proposed. The proposed algorithm has been employed to find the optimal grinding and dressing condition using the grinding process models and nonlinear grinding constraints. The optimized results also presents the guide line of grinding process. The effectiveness of the proposed algorithm is verified through the experimental results.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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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.

• Smart Structures and Systems
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• v.8 no.4
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• pp.343-365
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• 2011

Structural system identification (SSI) is an inverse problem of difficult solution. Currently, difficulties lie in the development of algorithms which can cater to large size problems. In this paper, a parameter estimation technique based on evolutionary strategy is presented to overcome some of the difficulties encountered in using the traditional system identification methods in terms of convergence. In this paper, a non-traditional form of system identification technique employing evolutionary algorithms is proposed. In order to improve the convergence characteristics, it is proposed to employ immune algorithms which are proved to be built with superior diversification mechanism than the conventional evolutionary algorithms and are being used for several practical complex optimisation problems. In order to reduce the number of design variables, domain decomposition methods are used, where the identification process of the entire structure is carried out in multiple stages rather than in single step. The domain decomposition based methods also help in limiting the number of sensors to be employed during dynamic testing of the structure to be identified, as the process of system identification is carried out in multiple stages. A fifteen storey framed structure, truss bridge and 40 m tall microwave tower are considered as a numerical examples to demonstrate the effectiveness of the domain decomposition based structural system identification technique using immune algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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• pp.987-992
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• 2002

In an attempt to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. In this paper, pareto-based Continuous Evolutionary Algorithms for Multiobjective Optimization problems having continuous search space are introduced. This algorithm is based on Continuous Evolutionary Algorithms to solve single objective optimization problems with a continuous function and continuous search space efficiently. For multiobjective optimization, a progressive reproduction operator and a niche-formation method fur fitness sharing and a storing process for elitism are implemented in the algorithm. The operator and the niche formulation allow the solution set to be distributed widely over the Pareto-optimal tradeoff surface. Finally, the validity of this method has been demonstrated through a numerical example.