• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.67-75
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• 1998

In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.223-226
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• 1997

Usually, the Evolutionary Algorithms(EAs) are considered more efficient for optimal, system design because EAs can provide higher opportunity for obtaining the global optimal solution. This paper presents a mechanism of co-evolution consists of the two genetic algorithms(GAs). This mechanism includes host populations and parasite populations. These two populations are closely related to each other, and the parasite populations plays an important role of searching for useful schema in host populations. Host population represented by feedforward neural network and the result of co-evolution we will find the optimal structure of the neural network. We used the genetic algorithm that search the structure of the feedforward neural network, and evolution strategies which train the weight of neuron, and optimize the net structure. The validity and effectiveness of the proposed method is exemplified on the stabilization and position control of the inverted-pendulum system.

• Animal cells and systems
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• v.11 no.2
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• pp.93-98
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• 2007

Gonadotropin-releasing hormone (GnRH), synthesized in the hypothalamus, plays a pivotal role in the regulation of vertebrate reproduction. Since molecular isoforms of GnRH and their receptors (GnRHR) have been isolated in a broad range of vertebrate species, GnRH and GnRHR provide an excellent model for understanding the molecular co-evolution of a peptide ligand-receptor pair. Vertebrate species possess multiple forms of GnRH, which have been created through evolutionary mechanisms such as gene/chromosome duplication, gene deletion and modification. Similar to GnRHs, GnRH receptors (GnRHR) have also been diversified evolutionarily. Comparative ligand-receptor interaction studies for non-mammalian and mammalian GnRHRs combined with mutational mapping studies of GnRHRs have aided the identification of domains or motifs responsible for ligand binding and receptor activation. Here we discuss the molecular basis of GnRH-GnRHR co-evolution, particularly the structure-function relationship regarding ligand selectivity and signal transduction of mammalian and non-mammalian GnRHRs.

• Genomics & Informatics
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• v.19 no.2
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• pp.15.1-15.7
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• 2021

The direction of evolution can estimate based on the variation among nonsynonymous to synonymous substitution. The simulative study investigated the nucleotide sequence of closely related strains of respiratory syndrome viruses, codon-by-codon with maximum likelihood analysis, z selection, and the divergence time. The simulated results, dN/dS > 1 signify that an entire substitution model tends towards the hypothesis's positive evolution. The effect of transition/transversion proportion, Z-test of selection, and the evolution associated with these respiratory syndromes, are also analyzed. Z-test of selection for neutral and positive evolution indicates lower to positive values of dN-dS (0.012, 0.019) due to multiple substitutions in a short span. Modified Nei-Gojobori (P) statistical technique results also favor multiple substitutions with the transition/transversion rate from 1 to 7. The divergence time analysis also supports the result of dN/dS and imparts substantiating proof of evolution. Results conclude that a positive evolution model, higher dN-dS, and transition/transversion ratio significantly analyzes the evolution trend of severe acute respiratory syndrome coronavirus 2, severe acute respiratory syndrome coronavirus, and Middle East respiratory syndrome coronavirus.

• Korean Journal of Agricultural and Forest Meteorology
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• v.5 no.2
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• pp.110-115
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• 2003

The iufluence of abandonment of agricultural fields on soil carbon and nitrogen dynamics is rarely addressed due to lack of appropriately paired sites. In this study, we identified three sites that have native forest and abandoned rice and crop fields at Mt. Kumdan near Seoul. Currently the vegetation of indigenous forest and the abandoned rice field is deciduous hardwood forest, while that of the abandoned crop field is deciduous shrub. We measured soil $CO_2$ evolution and inorganic N availability for the three sites from 25 July 2002 through 24 January 2003. Soil $CO_2$ evolution tracked seasonal soil temperature. Mean soil $CO_2$ evolution (g $CO_2$/$m^2$/hr) for the study period was 0.42 for the rice field to forest, 0.50 for the crop field to shrub, and 0.41 for the indigenous forest, respectively. Soil $CO_2$ evolution and soil temperature were not different among the sites; however, soil water content was significantly different. Soil water content had a very weak influence on soil $CO_2$ evolution. Inorganic resin N availability differed among the three sites and seemed to be related to soil moisture.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.375-381
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• 2005

Usually genetic algorithms are used to design optimal system. However the performance of the algorithm is determined by the fitness function and the system environment. It is expected that a co-evolutionary algorithm, two populations are constantly interact and co-evolve, is one of the solution to overcome these problems. In this paper we propose three types of co-evolutionary algorithm to solve GA-Hard problem. The first model is a competitive co-evolutionary algorithm that solution and environment are competitively co-evolve. This model can prevent the solution from falling in local optima because the environment are also evolve according to the evolution of the solution. The second algorithm is schema co-evolutionary algorithm that has host population and parasite (schema) population. Schema population supply good schema to host population in this algorithm. The third is game model-based co-evolutionary algorithm that two populations are co-evolve through game. Each algorithm is applied to visual servoing, robot navigation, and multi-objective optimization problem to verify the effectiveness of the proposed algorithms.

• Journal of Electrochemical Science and Technology
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• v.14 no.3
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• pp.243-251
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• 2023

Developing oxygen evolution reaction (OER) electrocatalysts is essential to accomplish viable CO₂ and water electrolysis. Herein, we report the fabrication and OER performance of Ni-foam (NF)-immobilized Ni₆ nanoclusters (NCs) (Ni₆/NF) prepared by a dip-coating process. The Ni₆/NF electrode exhibited a high current density of 500 mA/cm² for the OER at an overpotential as low as 0.39 V. Ni₆/NF exhibited high durability in an alkaline solution without corrosion. Electrokinetic studies revealed that OER can be easily initiated on Ni6 NC with fast electron-transfer rates. Finally, we demonstrated stable CO₂-to-CO electroreduction using an NC-based zero-gap CO₂ electrolyzer operated at a current density of 100 mA/cm² and a full-cell potential of 2.0 V for 12 h.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.4
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• pp.628-641
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• 2012

This research investigates a dynamic mechanism underlying the co-evolution between network and space by applying hype-curve model, typical phenomenon which shows how new technologies and ideas initially adapted in the society. This study analysis the knowledge intensive industry of digital contents using social network analysis (SNA) in terms of structural, spatial, and temporal aspects, year of 2000, 2005, and 2010 focused on Seoul area. First of all, network and space establish 'inter-feedback' as a result of evolution and differentiation process. Second, it happen temporal 'delay' through the learning process stage of 'peak of inflated expectation' and 'trough of disillusionment.' As a result, Seoul develops with the technology commercialized-orient strategy affect government policy. This trend changes to technology-oriented development in Seoul area in the late of 2000 established 'self-organization' with geographical proximity organizations through learning process.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.111.3-111
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• 2002

$\textbullet$introduction $\textbullet$CO-evolution Algorithm $\textbullet$Subsumption Architecture $\textbullet$Neural Network $\textbullet$Khepera Robot

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.130-132
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• 1994

Recently, a Deflection Yoke(DY) is designed in the bobbin-seperator-coil-winding type for high-definite CRT and high-efficient DY of wide vision TV or High Definite TV. This paper presents an optimization or bobbin-seperator-coil-winding type yoke's coil distribution for minimizing gap between desired and practical deflections of electron beams using by Evolution Strategy.