• Journal of Life Science
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• v.30 no.10
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• pp.851-859
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• 2020

Saururus chinensis has white roots, leaves, and flowers and is known to have antibacterial activity and anti-cancer efficacy. The aim of this study was to investigate the effect of the ethyl acetate fraction of a methanol extract of Saururus chinensis (SCEA) on antioxidant activity and melanin synthesis. SCEA at 64 ㎍/ml showed 62% of the DPPH radical scavenging activity of vitamin C, and its reducing power was 33% greater than that of vitamin C. Tyrosinase activity was 26% higher and melanin synthesis was 44% higher in the presence of SCEA at 64 ㎍/ml than in a blank group in a dopa oxidation assay. MTT assay showed that SCEA displayed cytotoxicity above 0.5 ㎍/ml, and SCEA at 1 ㎍/ml increased melanin synthesis by 69% in live B16F1 cells. SCEA was also separated into 13 fractions by silica column chromatography, and fraction 2 (Fr. 2) showed the highest DPPH radical scavenging activity, reducing power, and melanin synthesis. SCEA also promoted melanin production in live cells. LC-MASS analysis showed that Fr.2 had a molecular weight of 239, and these findings suggest that SCEA could be available for the promotion of melanin synthesis in black hair.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1013-1016
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• 1999

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA does well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in GA-hard problems and deceptive problems. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we propose an extended schema theorem associated with a schema co-evolutionary algorithm(SCEA), which explains why the co-evolutionary algorithm works better than SGA. The experimental results show that the SCEA works well in optimization problems including deceptive functions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.134-137
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• 2000

Genetic algorithms(GAs) have been widely used as a method to solve optimization problems. This is because GAs have simple and elegant tools with reproduction, crossover, and mutation to rapidly discover good solutions for difficult high-dimensional problems. They, however, do not guarantee the convergence of global optima in GA-hard problems such as deceptive problems. Therefore we proposed a Schema Co-Evolutionary Algorithm(SCEA) and derived extended schema 76988theorem from it. Using co-evolution between the first population made up of the candidates of solution and the second population consisting of a set of schemata, the SCEA works better and converges on global optima more rapidly than GAs. In this paper, we show advantages and efficiency of the SCEA by applying it to some problems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.353-356
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• 2004

비선형 시스템의 제어에서 널리 사용되는 방식이 퍼지 제어기이다. 퍼지 제어기에서 가장 중요한 것은 퍼지 룰의 설계이다. 퍼지 룰을 설계하는 많은 기법들이 제안되어 있는데, 최근 들어 진화 알고리즘에 대한 관심이 증가하고 있다 그 중에서도 공생적 공진화 알고리즘이 최적의 퍼지룰을 찾기 위해 이용되는데, 본 논문에서는 스키마 공진화 알고리즘을 이용한다. 스키마 공진화 알고리즘의 성능을 입증하기 위해, 이동 로봇의 행동제어를 위한 퍼지 제어기를 스키마 공진화 알고리즘을 이용하여 설계하고, 다른 공생적 공진화 알고리즘인 바이러스_진화 유전 알고리즘과 Handa의 공진화에 대해 비교하고 실험한다.