• 한국경영과학회지
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• 제28권2호
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• pp.1-15
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• 2003

This paper presents a new evolutionary algorithm to solve complex multi-level integration problems, which is called multi-level symbiotic evolutionary algorithm (MEA). The MEA uses an efficient feedback mechanism to flow evolution information between and within levels, to enhance parallel search capability, and to improve convergence speed and population diversity. To show the MEA's applicability, It is applied to the integrated planning of production and distribution in supply chain management. The encoding and decoding methods are devised for the integrated problem. A set of experiments has been carried out, and the results are reported. The superiority of the algorithm's performance is demonstrated through experiments.

• 한국게임학회 논문지
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• 제12권4호
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• pp.3-12
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• 2012

본 논문은 소셜게임에서 플레이어들이 어떻게 사회적 관계를 시작하고 유지, 순환시키는 구조를 만들어내는지에 대한 양상 연구를 그 목적으로 한다. 소셜게임에서 플레이어들은 상호 협력하여 도움을 주고받는다. 본 논문에서는 이 협력의 구조가 진화심리학과 신화학의 호혜 이론에 기반하고 있음을 착안, <팜빌>, <씨티빌>, <엠파이어즈 앤 얼라이언스>, <타이니 팜> 등의 텍스트 분석을 통해 고정성, 상호 유상성, 지속성, 전시성이라는 소셜게임의 협력 매커니즘을 도출해내고 이 호혜성의 게임 기제들이 게임 시스템에 어떻게 적용, 플레이되고 있는지를 증명하고자 하였다. 이와 같은 연구는 소셜게임을 포함한 디지털 게임에서 플레이어들 간의 협력적 스토리텔링을 창발시키는 게임 시스템을 개발하는 유의미한 지표를 제공한다는데 그 의의를 갖는다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4502-4517
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• 2019

Tone Injection (TI) scheme significantly reduces the peak-to-average power ratio (PAPR) of Multicarrier Modulation (MCM). However, the computational complexity of the TI scheme rises exponentially with the extra freedom constellation number. Therefore, a novel immune evolutionary mechanism-based TI scheme is proposed in this paper to reduce the computational complexity. By restraining undesirable degeneracy during the processing, this IEM scheme can dramatically increase the population fitness. Monte Carlo results show that proposed IEM-based TI scheme can achieve a significant PAPR and BER improvement with a low complexity.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.107-110
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• 2000

In this paper, we propose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series.

• Journal of Microbiology and Biotechnology
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• 제19권11호
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• pp.1301-1305
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• 2009

The evolution of living organisms occurs via a combination of highly complicated processes that involve modification of various features such as appearance, metabolism and sensing systems. To understand the evolution of life, it is necessary to understand how each biological feature has been optimized in response to new environmental conditions and interrelated with other features through evolution. To accomplish this, we constructed contents-based trees for a two-component system (TCS) and metabolic network to determine how the environmental communication mechanism and the intracellular metabolism have evolved, respectively. We then conducted a comparative analysis of the two trees using ARACNE to evaluate the evolutionary and functional relationship between TCS and metabolism. The results showed that such integrated analysis can give new insight into the study of bacterial evolution.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.224-227
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• 2000

In this Paper, we prepose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series, Sun spot data and KOSPI data.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.420-426
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• 1998

Evolutionary algorithms are probabilistic optimization algorithms based on the model of natural evolution. Recently the efforts to improve the performance of evolutionary algorithms have been made extensively. In this paper, we introduce the research for improving the convergence rate and search faculty of evolution algorithms by using reinforcement learning. After providing an introduction to evolution algorithms and reinforcement learning, we present adaptive genetic algorithms, reinforcement genetic programming, and reinforcement evolution strategies which are combined with reinforcement learning. Adaptive genetic algorithms generate mutation probabilities of each locus by interacting with the environment according to reinforcement learning. Reinforcement genetic programming executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. Reinforcement evolution strategies use the variances of fitness occurred by mutation to make the reinforcement signals which estimate and control the step length.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1500-1517
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• 2024

With the rapid development of the tourism economy, large-scale construction of tourist attractions to achieve resource utilization and ensure the healthy development of the tourism industry has become a hot topic. However, there are still issues with resource utilization and coordinated management in the economic development of the tourism industry, which in turn affects the coordinated development of the tourism industry economy. Therefore, this study utilizes self-organization theory to explore the structure, organizational mechanism, conditional driving force of evolution, and evolutionary operation mechanism of the tourism system, analyze the current tourism situation in Hunan Province and related regions, and construct a self-organization evolution model of the tourism system. The result shows that the cumulative variance contribution rate of tourism areas in Hunan Province is 78.8%, with Zhangjiajie having the highest industrial management factors and tourism resource levels in the province, with 1.6 and 3.2 respectively. Hunan Province has abundant tourism resources but overall uneven development, with a comprehensive score of -1.03. Therefore, it is necessary to leverage the coordination advantages of various departments and industries to promote sustainable and healthy development of tourism areas. The self-organizing evolution of the tourism system not only discovers the current problems of the tourism industry, but also provides theoretical support and mechanism suggestions for the tourism system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권4호
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• pp.1128-1145
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• 2022

Owing to the rapid development of information science, data analysis based on machine learning has become an interdisciplinary and strategic area. Marine predators algorithm (MPA) is a novel metaheuristic algorithm inspired by the foraging strategies of marine organisms. Considering the randomness of these strategies, an improved algorithm called co-evolutionary cultural mechanism-based marine predators algorithm (CECMPA) is proposed. Through this mechanism, search agents in different spaces can share knowledge and experience to improve the performance of the native algorithm. More specifically, CECMPA has a higher probability of avoiding local optimum and can search the global optimum quickly. In this paper, it is the first to use CECMPA to perform feature subset selection and optimize hyperparameters in support vector machine (SVM) simultaneously. For performance evaluation the proposed method, it is tested on twelve datasets from the university of California Irvine (UCI) repository. Moreover, the coronavirus disease 2019 (COVID-19) can be a real-world application and is spreading in many countries. CECMPA is also applied to a COVID-19 dataset. The experimental results and statistical analysis demonstrate that CECMPA is superior to other compared methods in the literature in terms of several evaluation metrics. The proposed method has strong competitive abilities and promising prospects.

• 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.