• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2004년도 추계학술대회
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• pp.171-178
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• 2004

This study suggests a general paradigm enhancing genetic mutability. Mutability among heterogeneous members in a genetic population has been a major problem in application of genetic programming to diverse business problems. This suggested paradigm is implemented to developing new methods from existing methods. Within the evolutionary approach taken to designing new methods, a general representation scheme of the genetic programming framework, called a kernel, is introduced. The kernel is derived from the literature of algorithms and heuristics for combinatorial optimization problems. The commonality and differences among these methods have been identified and again combined by following the genetic inheritance merging them. The kernel was tested for selected methods in combinatorial optimization. It not only duplicates the methods in the literature, it also confirms that each of the possible solutions from the genetic mutation is in a valid form, a running program. This evolutionary method suggests diverse hybrid methods in the form of complete programs through evolutionary processes. It finally summarizes its findings from genetic simulation with insight.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.376-379
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• 2003

DNA computing based DNA sequence Is operated through the biology experiment. Biology experiment used as operator causes illegal reactions through shifted hybridization, mismatched hybridization, undesired hybridization of the DNA sequence. So, it is essential to design DNA sequence to minimize the potential errors. This paper proposes method of the DNA sequence generation based evolutionary operation processor. Genetic algorithm was used for evolutionary operation and extra hardware, namely genetic algorithm processor was implemented for solving repeated evolutionary process that causes much computation time. To show efficiency of the Proposed processor, excellent result is confirmed by comparing between fitness of the DNA sequence formed randomly and DNA sequence formed by genetic algorithm processor. Proposed genetic algorithm processor can reduce the time and expense for preparing DNA sequence that is essential in DNA computing. Also it can apply design of the oligomer for development of the DNA chip or oligo chip.

• Smart Structures and Systems
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• 제8권4호
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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.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2000년도 추계학술대회 및 정기총회
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• pp.113-116
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• 2000

The purpose of this study is to present a method for determining the k most vital arcs in shortest path problem using an evolutionary algorithm. The problem of finding the k most vital arcs in shortest path problem is to find a set of k arcs whose simultaneous removal from the network causes the greatest increase in the total length of shortest path. The problem determining the k most vital arcs in shortest path problem has known as NP-hard. Therefore, in order to deal with the problem of real world the heuristic algorithm is needed. In this study we propose to the method of finding the k-MVA in shortest path problem using an evolutionary algorithm which known as the most efficient algorithm among heuristics. For this, the expression method of individuals compatible with the characteristics of shortest path problem, the parameter values of constitution gene, size of the initial population, crossover rate and mutation rate etc. are specified and then the effective genetic algorithm will be proposed. The method presented in this study is developed using the library of the evolutionary algorithm framework (EAF) and then the performance of algorithm is analyzed through the computer experiment.

• 한국시뮬레이션학회논문지
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• 제27권2호
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• pp.101-114
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• 2018

무인 비행체의 실시간 경로계획 생성 시 최적의 경로를 찾기 위한 다양한 연구가 진행되어 왔다. 본 논문에서는 진화알고리즘을 통한 무인비행체의 경로계획 생성을 수행하고, 이를 에이전트 기반 시뮬레이션 환경에서 구현 및 테스트가 가능함을 검증하였다. 이를 위해, Repast toolkit에 JGAP 패키지를 탑재하여 Java 기반의 유전 알고리즘 프로그래밍을 통한 무인 비행체의 경로 계획을 생성하였고, 해당 결과를 에이전트 기반으로 시뮬레이션을 수행하였다. 본 논문에서는 에이전트 기반 시뮬레이션 소프트웨어를 소프트웨어 공학 개발 생명주기에 맞춰 문서화하여 설계 및 구현되었으며, 에이전트 모델링 설계는 자동화, 적응성 및 에이전트 간의 상호 작용에 초점을 맞추었다. 또한, 시뮬레이션을 통해 에이전트 기반 환경에서 설계한 모델 및 시나리오를 검증하여 다수의 비행 에이전트에 내재된 동적 경로계획 알고리즘이 실시간으로 자율적인 경로 생성이 가능함을 증명하였다.

• Journal of Ecology and Environment
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• 제33권4호
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• pp.275-288
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• 2010

Ecological modeling faces some unique problems in dealing with complex environment-organism relationships, making it one of the toughest domains that might be encountered by a modeler. Newer technologies and ecosystem modeling paradigms have recently been proposed, all as part of a broader effort to reduce the uncertainty in models arising from qualitative and quantitative imperfections in the ecological data. In this paper, evolutionary computation modeling approaches are introduced and proposed as useful modeling tools for ecosystems. The results of our case study support the applicability of an algal predictive model constructed via genetic programming. In conclusion, we propose that evolutionary computation may constitute a powerful tool for the modeling of highly complex objects, such as river ecosystems.

• 대한전기학회논문지:전력기술부문A
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• 제51권11호
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• pp.544-550
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• 2002

This paper describes a strategy of a daily optimal operational scheduling on the industrial cogeneration system. The cogeneration system selected to establish the scheduling consists of three units and several auxiliary devices which include three auxiliary boilers, t재 waste boilers and three sludge incinerators. One unit generated electrical and thermal energy using the back pressure turbine. The other two units generate the energy using the extraction condensing turbine. Three auxiliary devices operate to supply energy to the loads with three units. The cogeneration system is able to supply enough the thermal energy to the thermal load, however it can not sufficiently supply the electric energy to the electrical load. Therefore the insufficient electric energy is compensated by buying electrical energy from utility. In this paper, the evolutionary algorithms was applied to establish the optimal scheduling for the cogeneration systems. Also the GUI System was developed using established mathematics medeling and evolutionary algorithms in order that non-experts are able to establish operational scheduling. This results revel that the proposed modeling and strategy can be effectively applied to cogeneration system for paper mill.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3293-3311
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• 2015

Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users' preferences. The results of the simulation also show that our approach can achieve a better scalability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.80-96
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• 2022

Nowadays, artificial intelligence promotes the rapid development of skin cancer detection technology, and the federated skin cancer detection model (FSDM) and dual generative adversarial network model (DGANM) solves the fragmentation and privacy of data to a certain extent. To overcome the problem that the many-objective evolutionary algorithm (MaOEA) cannot guarantee the convergence and diversity of the population when solving the above models, a many-objective evolutionary algorithm based on integrated strategy (MaOEA-IS) is proposed. First, the idea of federated learning is introduced into population mutation, the new parents are generated through sub-populations employs different mating selection operators. Then, the distance between each solution to the ideal point (SID) and the Achievement Scalarizing Function (ASF) value of each solution are considered comprehensively for environment selection, meanwhile, the elimination mechanism is used to carry out the select offspring operation. Eventually, the FSDM and DGANM are solved through MaOEA-IS. The experimental results show that the MaOEA-IS has better convergence and diversity, and it has superior performance in solving the FSDM and DGANM. The proposed MaOEA-IS provides more reasonable solutions scheme for many scholars of skin cancer detection and promotes the progress of intelligent medicine.

• BMB Reports
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• 제52권4호
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• pp.227-238
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• 2019

GROWTH-REGULATING FACTORs (GRFs) are sequence-specific DNA-binding transcription factors that regulate various aspects of plant growth and development. GRF proteins interact with a transcription cofactor, GRF-INTERACTING FACTOR (GIF), to form a functional transcriptional complex. For its activities, the GRF-GIF duo requires the SWITCH2/SUCROSE NONFERMENTING2 chromatin remodeling complex. One of the most conspicuous roles of the duo is conferring the meristematic potential on the proliferative and formative cells during organogenesis. GRF expression is post-transcriptionally down-regulated by microRNA396 (miR396), thus constructing the GRF-GIF-miR396 module and fine-tuning the duo's action. Since the last comprehensive review articles were published over three years ago, many studies have added further insight into its action and elucidated new biological roles. The current review highlights recent advances in our understanding of how the GRF-GIF-miR396 module regulates plant growth and development. In addition, I revise the previous view on the evolutionary origin of the GRF gene family.