• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권7호
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• pp.3501-3523
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• 2017

It is a classical integer optimization difficulty to design an optimal selection scheme in cooperative relay networks considering co-channel interference (CCI). In this paper, we solve single-objective and multi-objective relay selection problem. For the single-objective relay selection problem, in order to attain optimal system performance of cooperative relay network, a novel quantum differential evolutionary algorithm (QDEA) is proposed to resolve the optimization difficulty of optimal relay selection, and the proposed optimal relay selection scheme is called as optimal relay selection based on quantum differential evolutionary algorithm (QDEA). The proposed QDEA combines the advantages of quantum computing theory and differential evolutionary algorithm (DEA) to improve exploring and exploiting potency of DEA. So QDEA has the capability to find the optimal relay selection scheme in cooperative relay networks. For the multi-objective relay selection problem, we propose a novel non-dominated sorting quantum differential evolutionary algorithm (NSQDEA) to solve the relay selection problem which considers two objectives. Simulation results indicate that the proposed relay selection scheme based on QDEA is superior to other intelligent relay selection schemes based on differential evolutionary algorithm, artificial bee colony optimization and quantum bee colony optimization in terms of convergence speed and accuracy for the single-objective relay selection problem. Meanwhile, the simulation results also show that the proposed relay selection scheme based on NSQDEA has a good performance on multi-objective relay selection.

• Structural Engineering and Mechanics
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• 제11권5호
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• pp.485-502
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• 2001

Modem soft computing methods, such as neural networks, evolutionary models and fuzzy logic, are mainly inspired by the problem solving strategies the biological systems use in nature. As such, the soft computing methods are fundamentally different from the conventional engineering problem solving methods, which are based on mathematics. In the author's opinion, these fundamental differences are the key to the full understanding of the soft computing methods and in the realization of their full potential in engineering applications. The main theme of this paper is to discuss the fundamental differences between the soft computing methods and the mathematically based conventional methods in engineering problems, and to explore the potential of soft computing methods in new ways of formulating and solving the otherwise intractable engineering problems. Inverse problems are identified as a class of particularly difficult engineering problems, and the special capabilities of the soft computing methods in inverse problems are discussed. Soft computing methods are especially suited for engineering design, which can be considered as a special class of inverse problems. Several examples from the research work of the author and his co-workers are presented and discussed to illustrate the main points raised in this paper.

• 기술혁신학회지
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• 제19권1호
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• pp.80-104
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• 2016

기존 유망 신기술에 대한 기술기획의 방법론들은 대상 기술 자체에 초점을 두어 해당 기술이 영향을 미치는 사회 환경적 맥락 이해가 부족한 현실이다. 이에 본 연구는 과학 커뮤니케이션 분야에서 널리 쓰이고 있는 뉴스 내용분석 방법론이 유망 신기술 대한 기술기획에서 사회적 환경의 맥락적 이해를 위한 보완적 방법론으로 활용될 수 있음을 살펴보고자 한다. 기술-사회 공진화 환경에서 유망 신기술은 사회와의 관계에서 하이프(hype) 현상을 나타낸다. 이에 착안하여 뉴스 내용분석을 수행하여 해당 분석 결과가 하이프 사이클 궤적과 부합하는 지를 탐색적으로 살피고, 뉴스 프레임 분석을 통해서는 유망 신기술에 따른 사회적 가시성의 실체적 내용을 이해하고자 하였다. 이를 위해 대표적인 유망 신기술로 클라우드 컴퓨팅을 대상으로 뉴스 내용분석을 수행하였다. 종합지, 경제지 및 IT전문지를 대상으로 한 뉴스 내용분석 결과는 뉴스 보도빈도가 가트너가 제시한 하이프 사이클 궤적과 부합하였으며, 특히 보도 태도 및 뉴스 프레임 분석은 유망 신기술에 대한 거시 환경요인별 맥락적 내용을 이해할 수 있는 유용한 정보를 제공함을 확인하였다. 본 연구의 결과는 뉴스 내용분석이 기술기획에서 주로 활용되는 논문 특허를 중심으로 한 기술정보 분석의 한계점을 극복하고 거시환경 요인별 맥락 이해를 위한 보완적 방법론으로 활용될 수 있음을 시사한다. 결론적으로 기술기획의 거시환경 분석 단계에서 뉴스 내용분석 방법론의 활용은 기술-사회 공진화 관점의 상호 균형된 시각을 확보함에 기여할 수 있을 것이다.