• 한국동물학회:학술대회논문집
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• 한국동물학회 1998년도 한국생물과학협회 학술발표대회
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• pp.342.2-342
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• 1998

No Abstract, See Full Text

• 한국작물학회:학술대회논문집
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• 한국작물학회 2005년도 추계학술발표회 요지
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• pp.258-259
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• 2005

• 한국지능시스템학회논문지
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• 제17권1호
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• pp.86-93
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• 2007

A new operation termed competitive generation in the processes of genetic algorithms is proposed for accelerating the optimization speed of genetic algorithms. The competitive generation devised by considering the competition of sperms for fertilization provides a good opportunity for the genetic algorithms to approach global optimum without falling into local optimum. Experimental results with typical problems showed that the genetic algorithms with competitive generation are superior to those without the competitive generation.

• 한국독성학회:학술대회논문집
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• 한국독성학회 2004년도 KSOT/KEMS fall annual meeting
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• pp.19-20
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• 2004

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(3)
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• pp.9-12
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• 2000

Genetic algorithm is a searching method which based on the law of the survival of the fittest. Multi-population Genetic Algorithm is a modified form of Genetic Algorithm, which was devised for covering the defect of general genetic algorithm. The core of multi-population genetic algorithm is said to be the migration schemes. The fitness-based migration scheme and the random migration scheme are currently used. In this paper, a new migration scheme, ‘the migration scheme between groups’, is suggested, and compared to the general two migration schemes.

• 한국약용작물학회:학술대회논문집
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• 한국약용작물학회 2007년도 추계학술발표회
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• pp.287.2-287.2
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• 2007

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.255-255
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• 2000

Technical advance of electrical chart and cruising equipment make it possible to sail without a man. It is important to decide the cruising route in view of effectiveness and stability of a ship. So we need to study on the optimal trajectory planning. Genetic algorithm is a strong optimization algorithm with adaptational random search. It is a good choice to apply genetic algorithm to the trajectory planning of a ship. We modify a genetic algorithm to solve this problem. The effectiveness of the revised genetic algorithm is assured through computer simulations.

• 한국수정란이식학회:학술대회논문집
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• 한국수정란이식학회 2006년도 The 6th Intermational Symposium on Developmental Biotechnology
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• pp.124-125
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• 2006

• 한국독성학회:학술대회논문집
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• 한국독성학회 2005년도 춘계 국제심포지엄 및 학술대회
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• pp.178-178
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• 2005

• 한국식물생명공학회:학술대회논문집
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• 한국식물생명공학회 2004년도 생명공학 실용화를 위한 비젼
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• pp.147-147
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• 2004