• Molecules and Cells
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• 제26권1호
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• pp.1-4
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• 2008

A gene's influence on an organism's Darwinian fitness ultimately determines whether it will be lost, maintained or modified by natural selection, yet biologists have few gene expression systems in which to measure whole-organism gene function. In the Department of Molecular Ecology at the Max Planck Institute for Chemical Ecology we are training "molecularly enabled field biologists" to use transformed plants silenced in the expression of environmentally regulated genes and the plant's native habitats as "laboratories." Research done in these natural laboratories will, we hope, increase our understanding of the function of genes at the level of the organism. Examples of the role of threonine deaminase and RNA-directed RNA polymerases illustrate the process.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제2권1호
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• pp.95-110
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• 1998

Evolutionary Algorithms (EAs) are population-based optimization methods based on the principle of Darwinian natural selection. The representative methodology in EAs is genetic algorithm (GA) proposed by J. H. Holland, and the theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. In the meaning of these foundational concepts, simple genetic algorithm (SGA) allocate more trials to the schemata whose average fitness remains above average. Although SGA does well in many applications as an optimization method, still it does not guarantee the convergence of 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 contrast with traditional single population evolutionary algorithm. In this paper we show why the co-evolutionary algorithm works better than SGA in terms of an extended schema theorem. And predator-prey co-evolution and symbiotic co-evolution, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. And the experimental results show a co-evolutionary algorithm works well in optimization problems even though in deceptive functions.

• 한국응용곤충학회지
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• 제61권1호
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• pp.129-142
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• 2022

영양은 모든 생명활동의 근본이며, 생물의 진화적 적응도를 결정하는 가장 중요한 요인이다. 곤충영양학은 곤충생리학의 전통적인 연구영역이며, 최근 산업곤충의 대량사육 필요성이 증가함에 따라 그 중요성이 부각되고 있다. 이러한 중요성에도 불구하고, 곤충의 영양현상을 정확히 이해하기란 어려운데, 이는 영양의 다변량적 특성, 영양소 간의 교호작용 등으로 설명되는 영양적 복잡성에 기인한다. 영양기하학(Nutritional Geometry)은 이러한 난점을 극복하기 위해 고안된 통합적이고 다차원적인 분석모형으로서, 최근 곤충영양학이 급격하게 발전할 수 있는 이론적 및 실험적 기반을 제공하였다. 본 종설은 영양기하학의 기본개념을 소개하고, 이러한 방법론이 어떻게 최근 곤충영양학의 급속한 학문적 진보를 가능케 하였는지, 그리고 영양이 어떻게 생리학, 생태학, 진화생물학을 통합하는 구심점이 될 수 있었는지를, 최신 연구사례를 중심으로 살펴볼 것이다. 또한 본 종설은 향후 영양기하학을 적용함으로써 발전할 가능성이 높은 연구분야를 고찰할 것이다.