• Journal of Korean Society of Steel Construction
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• v.13 no.6
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• pp.673-681
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• 2001

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithm. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithm. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the design points selected form the genetic process. The evolutionary process evaluates the survivability of the design points. The evolutionary process evaluates the survivability of the design points selected form the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithm was verified by applying the algorithm to optimum design examples.

• Journal of Korean Association for Spatial Structures
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• v.1 no.1 s.1
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• pp.125-134
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• 2001

The objective of this study is the development of size discrete optimum design algorithm which is based on the GAs(genetic algorithms). The algorithm can perform size discrete optimum designs of space trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of space trusses and the constraints are limite state design codes(1998) and displacements. The basic search method for the optimum design is the GAs. The algorithm is known to be very efficient for the discrete optimization. This study solves the problem by introducing the GAs. The GAs consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. In the genetic process of the simple GAs, there are three basic operators: reproduction, cross-over, and mutation operators. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying GAs to optimum design examples.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.1
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• pp.61-73
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• 2012

The paper presents the novel approach to solve some problems occurred in application of the genetic algorithm to the determination of the optimal tire pattern sequence in order to reduce the tire air-pumping noise which is generated by the repeated compression and expansion of the air cavity between tire pattern and road surface. The genetic algorithm has been used to find the optimal tire pattern sequence having a low level of tire air-pumping noise using the image based air-pumping model. In the genetic algorithm used in the previous researches, there are some problems in the encoding structure and the selection of objective function. The paper proposed single encoding element with five integers, divergent objective function based on evolutionary process and the optimal evolutionary rate based on Shannon entropy to solve the problems. The results of the proposed genetic algorithm with evolutionary process are compared with those of the randomized algorithm without evolutionary process on the two-dimensional normal distribution. It is confirmed that the genetic algorithm is more effective to reduce the peak value of the predicted tire air-pumping noise and the consistency and cohesion of the obtained simulation results are also improved in terms of probability.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.106-115
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• 1999

Design parameters and goals play important roles in design. Design goals are the required functions of the design elements and explicitly expressed by design parameters. Design parameters also indicate the relations among design elements, by which constraint networks can be constructed and some useful information can be induced. In this study, the mechanical design process is assumed to be the assignment of design goals and their realization through the evolutionary refinement of the design parameters. Thus an integrated design system is proposed to support the process of assigning the design goals and refining the values of the design parameters. In the design system, a genetic engine that utilizes a genetic algorithm is installed to simulate an iterative design process, which leads to an evolutionary design. The genetic engine treats design parameters as genes and design goals as evaluation function. Re-design and design modification are facilitated by the design parameters. The re-design can be activated in the design system by using the information stored in the design parameters when design parameters or goals are changed.

• International Journal of Automotive Technology
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• v.7 no.7
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• pp.795-801
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• 2006

A No Spin Differential (NSD) design has been improved from evaluation of two NSD models utilizing the axiomatic approach. New design parameters of the second level are developed to satisfy the independence axiom. The design matrices are determined to decouple the relationship between design parameters and process parameters. The values of process parameters are then determined to optimize and improve the NSD design. Consequently a unique and evolutionary NSD design is achieved with the aid of the axiomatic approach.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.39-55
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• 2012

This paper presents an endosymbiotic evolutionary algorithm (EEA) to solve both problems of line balancing and model sequencing in a mixed-model two-sided assembly line (MMtAL) simultaneously. It is important to have a proper balancing and model sequencing for an efficient operation of MMtAL. EEA imitates the natural evolution process of endosymbionts, which is an extension of existing symbiotic evolutionary algorithms. It provides a proper balance between parallel search with the separated individuals representing partial solutions and integrated search with endosymbionts representing entire solutions. The strategy of localized coevolution and the concept of steady-state genetic algorithms are used to improve the search efficiency. The experimental results reveal that EEA is better than two compared symbiotic evolutionary algorithms as well as a traditional genetic algorithm in solution quality.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.93.1-93
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• 2001

An evolutionary approach for the design of Fuzzy Logic Systems(FLSs) is proposed. Membership functions(MFs) in Takagi-Sugeno type fuzzy logic system is optimized through evolutionary process. Output singleton values are obtained through pseudo-inverse method. The proposed technique is unique for that, to prevent overfilling phenomenon, limited-level RBF membership functions are used and the new fitness function is invented. To show the effectiveness of the proposed method, some simulations results on model identification are given.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.11
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• pp.625-631
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• 2000

In designing fuzzy model, we encounter a major difficulty in the identification of an optimized fuzzy rule base, which is traditionally achieved by a tedious-and-error process. This paper presents an approach to automatic design of optimal fuzzy rule bases for modeling using evolutionary programming. Evolutionary programming evolves simultaneously the structure and the parameter of fuzzy rule base a given task. To check the effectiveness of the suggested approach, 3 examples for modeling are examined, and the performance of the identified models are demonstrated.

• Journal of Korea Technology Innovation Society
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• v.7 no.3
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• pp.607-640
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• 2004

This article analyzes the venture firm based upon the new framework of Dynamic Firm Capability (DFC) to identify the process mechanism. Research methodology includes the case study involving structured interview and data collection from two leading Korean ICT(Information Communication Technology) firms in the same sector (DVR). IDIS, spun off from the university, has accumulated the innovative capability based on the R&D department. On the other hand, KODICOM has retained the technological trajectory in terms of marketing competence. Underlying hypothesis is that a firm should show a idiosyncratic evolutionary pattern by acquiring different complimentary assets(CA). In addition, effective internal process should be matched with the essential characteristics not only at the firm level but also at the sectoral level. By analyzing those two different firms, we will find the strategic successful factors based upon the evolutionary point of view. It is a key contribution of this paper to study on the process mechanism of ventures, and to explain detailed process mechanism by viewing two different characteristics of the firm at the functional level.

• 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.