• Molecules and Cells
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• v.26 no.1
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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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• v.2 no.1
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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.

• Korean journal of applied entomology
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• v.61 no.1
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• pp.129-142
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• 2022

Nutrition dictates nearly all biological processes and determines Darwinian fitness in all living organisms, including insects. Research on insect nutrition has a long history in the field of insect physiology and the importance of understanding insect nutrition has become increasingly apparent with the growing need for producing insects as food and feed. Nevertheless, it is only in recent years that we have witnessed a major breakthrough in our knowledge of insect nutrition. The multivariate, interactive, and dynamic nature of nutrition has long hampered our complete understanding of insect nutrition. However, the challenge posed by such nutritional complexity has been overcome with the advent of the Nutritional Geometry, which is an integrative and multidimensional framework that enabled us to model complex interactions between multiple nutrients. In this review, we introduce the basic concepts and principles of the Nutritional Geometry and describe how this innovative framework has revolutionized the field of insect nutrition and has placed nutrition in the centre of the interface between physiology, ecology, and evolution. We close this review by discussing potentially fertile research areas that can benefit tremendously from the application of this powerful nutritional paradigm in the future.