• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.239-242
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• 2000

Symbiotic evolutionary algorithms are stochastic search algorithms that imitate the biological coevolution process through symbiotic interactions. In the algorithms, the fitness evaluation of an individual requires first selecting symbiotic partners of the individual. The symbiotic partner effects the change of individual's fitness and search direction. In this study we are to analyze how much partnering strategies can influence the performance of the algorithms. For this goal extensive experiments are carried out to compare the performance of partnering strategies. The NKC model and the binary string covering problem are used as the test-bed problems. The experimental results indicate that there does not exist statistically significant difference in their performance.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.5
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• pp.188-202
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• 2004

This research was designed to investigate biomechanical aspects of the evolution based on the hypothesis of dynamic cooperative interactions between the locomotion pattern and the body shape in the evolution of human bipedal walking The musculoskeletal model used in the computer simulation consisted of 12 rigid segments and 26 muscles. The nervous system was represented by 18 rhythmic pattern generators. The genetic algorithm was employed based on the natural selection theory to represent the evolutionary mechanism. Evolutionary strategy was assumed to minimize the cost function that is weighted sum of the energy consumption, the muscular fatigue and the load on the skeletal system. The simulation results showed that repeated manipulations of the genetic algorithm resulted in the change of body shape and locomotion pattern from those of chimpanzee to those of human. It was suggested that improving locomotive efficiency and the load on the musculoskeletal system are feasible factors driving the evolution of the human body shape and the bipedal locomotion pattern. The hypothetical evolution method employed in this study can be a new powerful tool for investigation of the evolution process.

• Korean Business Review
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• v.11
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• pp.15-38
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• 1998

This study explores the evolution of technical innovation over time. It focuses on sectors of the information technology because this industry can be referred to as one of the most dynamic industries of all times. Following evolutionary theorists, we argue that technological change is gradual and that superior firms and technologies are reward by the' selection' environment. In the initial phase of the industry life cycle, technological change is expected to be radical and uncertainty is high. Over time a product or technology is likely to arise which stands out above all other products or technologies. These so-called 'basic designs' serve as sorts of 'technological guideposts' for further developments in the technology. Once a basic design established, technological progress tends to follow consistent paths or trajectories. The cumulative character of technological progress facilitates a rapid expansion of the boundaries of the technology until the natural limits of the technology are approached and technological progress slows down. Following ecological theories, supply-side developments in the industry are described on the basis of five different organizational types. On the basis of this pattern of market and technological evolution we came up with seven basic propositions.

• The Korean Journal of Zoology
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• v.30 no.3
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• pp.219-230
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• 1987

Two allelotypes of sMDH variation, namely A and B type, are known in the dark chub, Zacco temmincki. We attempted to clarify their probable functional enzymatic difEerence with temperature change. Two types of sMDH were purified separately by successive chromatography on DEAE-cellulose and blue 2-Sepharose amEnity columns, and their ensymatic activities to temperature change were measured. Q10 of Vmax and Vmax/Km were significantly different between two types, i.e. A type being higher in Q10 values than B type. Based on the result it is assumed that A type may be more sensitive to temperature change than B type.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.4
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• pp.312-326
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• 2021

The prey-predator model is one of the most influential mathematical models in ecology and evolutionary biology. In this study, we considered a modified prey-predator model, which describes the rate of change for each species. The effects of modifications to the classical prey-predator model are investigated here. The conditions required for the existence of the first integral and the stability of the fixed points are studied. In particular, it is shown that the first integral exists only for a subset of the model parameters, and the phase portraits around the fixed points exhibit physically relevant phenomena over a wide range of the parameter space. The results show that adding coupling terms to the classical model widely expands the dynamics with great potential for applicability in real-world phenomena.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.256-259
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• 2005

Among non-synonymous SNPs that cause amino acid change in the protein product, the selection of disease-causing SNPs has been of great interest. We present the comparison between the evolutionary (SIFT score) and structural information (binding pocket) to show that the incorporation between them provides an advantage of sorting disease-causing SNPs from normal SNPs. To set up the procedure, we apply the machine learning method to the test data set from the laboratory experiments.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.170-173
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• 2006

Requirement management is an essential process that has a significant influence on the project success. Due to the evolutionary development nature of requirement, the effective management for requirement must include change process and configuration control. This paper describes how to implement effective requirement management process to the Korea Tilting Train eXpress development with the application of a model based systems engineering tool.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.89-95
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• 2016

In a software testing domain, test case prioritization techniques improve the performance of regression testing, and arrange test cases in such a way that maximum available faults be detected in a shorter time. User-sessions and cookies are unique features of web applications that are useful in regression testing because they have precious information about the application state before and after making changes to software code. This approach is in fact a user-session based technique. The user session will collect from the database on the server side, and test cases are released by the small change configuration of a user session data. The main challenges are the effectiveness of Average Percentage Fault Detection rate (APFD) and time constraint in the existing techniques, so in this paper developed an intelligent framework which has three new techniques use to manage and put test cases in group by applying useful criteria for test case prioritization in web application regression testing. In dynamic weighting approach the hybrid criteria which set the initial weight to each criterion determines optimal weight of combination criteria by evolutionary algorithms. The weight of each criterion is based on the effectiveness of finding faults in the application. In this research the priority is given to test cases that are performed based on most common http requests in pages, the length of http request chains, and the dependency of http requests. To verify the new technique some fault has been seeded in subject application, then applying the prioritization criteria on test cases for comparing the effectiveness of APFD rate with existing techniques.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.161-165
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• 2014

In this paper, we proposed the fuzzy prototype pattern classifier. In the proposed classifier, each prototype is defined to describe the related sub-space and the weight value is assigned to the prototype. The weight value assigned to the prototype leads to the change of the boundary surface. In order to define the prototypes, we use Fuzzy C-Means Clustering which is the one of fuzzy clustering methods. In order to optimize the weight values assigned to the prototypes, we use the Differential Evolutionary Algorithm. We use Linear Discriminant Analysis to estimate the coefficients of the polynomial which is the structure of the consequent part of a fuzzy rule. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.767-773
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• 2014

An economic ship routing means to sail a ship with a goal of minimizing the fuel consumption by utilizing weather forecast information, and there have been various systems which have been recently studied. For a successful economic ship routing system, it is needed to properly control an engine power or change a geographical path considering weather forecast. An optimal geographical path is difficult to be determined, though, because it is a minimal dynamic-cost path search problem where the actual fuel consumption is dynamically variable by the weather condition when the ship will pass the area. In this paper, we propose an geographical path-search algorithm based on evolutionary strategy to efficiently search a good quality solution out of tremendous candidate solutions. We tested our approach with the shortest path-based sailing method over seven testing routes and observed that the former reduced the estimated fuel consumption than the latter by 1.82% on average and the maximum 2.49% with little difference of estimated time of arrival. In particular, we observed that our method can find a path to avoid bad weather through a case analysis.