• Molecules and Cells
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• v.26 no.5
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• pp.427-435
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• 2008

In this review, we discuss the genes and the related signal pathways that regulate aging and longevity by reviewing recent findings of genetic longevity models in rodents in reference to findings with lower organisms. We also paid special attention to the genes and signals mediating the effects of calorie restriction (CR), a powerful intervention that slows the aging process and extends the lifespan in a range of organisms. An evolutionary view emphasizes the roles of nutrient-sensing and neuroendocrine adaptation to food shortage as the mechanisms underlying the effects of CR. Genetic and non-genetic interventions without CR suggest a role for single or combined hormonal signals that partly mediate the effect of CR. Longevity genes fall into two categories, genes relevant to nutrient-sensing systems and those associated with mitochondrial function or redox regulation. In mammals, disrupted or reduced growth hormone (GH)-insulin-like growth factor (IGF)-1 signaling robustly favors longevity. CR also suppresses the GH-IGF-1 axis, indicating the importance of this signal pathway. Surprisingly, there are very few longevity models to evaluate the enhanced anti-oxidative mechanism, while there is substantial evidence supporting the oxidative stress and damage theory of aging. Either increased or reduced mitochondrial function may extend the lifespan. The role of redox regulation and mitochondrial function in CR remains to be elucidated.

• Earthquakes and Structures
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• v.14 no.1
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• pp.1-10
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• 2018

Seismic retrofitting of existing buildings and design of earth-quake resistant buildings are important issues associated with earthquake-prone zones. Use of metallic-yielding dampers as an energy dissipation system is an acceptable method for controlling damages in structures and improving their seismic performance. In this study, the optimal distribution of dampers for reducing the seismic response of steel frames with multi-degrees freedom is presented utilizing the uniform distribution of deformations. This has been done in a way that, the final configuration of dampers in the frames lead to minimum weight while satisfying the performance criteria. It is shown that such a structure has an optimum seismic performance, in which the maximum structure capacity is used. Then the genetic algorithm which is an evolutionary optimization method is used for optimal arrangement of the steel dampers in the structure. In continuation for specifying the optimal accurate response, the local search algorithm based on the gradient concept has been selected. In this research the introduced optimization methods are used for optimal retrofitting in the moment-resisting frame with inelastic behavior and initial weakness in design. Ultimately the optimal configuration of dampers over the height of building specified and by comparing the results of the uniform deformation method with those of the genetic algorithm, the validity of the uniform deformation method in terms of accuracy, Time Speed Optimization and the simplicity of the theory have been proven.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.2
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• pp.135-148
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• 2000

In this paper, we present a linear scheduling method for homogeneous multiprocessor systems using genetic algorithms. In general, genetic algorithms randomly generate initial strings, which leads to long operation time and slow convergence due to an inappropriate initialization. The proposed algorithm considers communication costs among processors and generates initial strings such that successive nodes are grouped into the same cluster. In the crossover and mutation operations, the algorithm maintains linearity in scheduling by associating a node with its immediate successor or predecessor. Linear scheduling can fully utilize the inherent parallelism of a given program and has been proven to be superior to nonlinear scheduling on a coarse grain DAG (directed acyclic graph). This paper emphasizes the usability of the genetic algorithm for real-time applications. Simulation results show that the proposed algorithm rapidly converges within 50 generations in most DAGs.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.11
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• pp.1559-1571
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• 2008

A rule was developed to constrain the annual rate of inbreeding to a predefined value in a population with different lifetimes between sires and dams, and to maximize the selection response over generations. This rule considers that the animals in a population should be divided into sex-age classes based on the theory of gene flow, and restricts the increase of average inbreeding coefficient for new offspring by limiting the increase of the mean additive genetic relationship for parents selected. The optimization problem of this rule was formulated as a quadratic programming problem. Inputs for the rule were the BLUP estimated breeding values, the additive genetic relationship matrix of all animals, and the long-term contributions of sex-age classes. Outputs were optimal number and contributions of selected animals. In addition, this rule was combined with the optimization of emphasis given to QTL, and further increased the genetic gain over the planning horizon. Stochastic simulations of closed nucleus schemes for pigs were used to investigate the potential advantages obtained from this rule by combining the standard QTL selection, optimal QTL selection and conventional BLUP selection. Results showed that the predefined rates of inbreeding were actually achieved by this rule in three selection strategies. The rule obtained up to 9.23% extra genetic gain over truncation selection at the same rates of inbreeding. The combination of the extended rule and the optimization of emphasis given to QTL allowed substantial increases in selection response at a fixed annual rate of inbreeding, and solved substantially the conflict between short-term and long-term selection response in QTL-assisted selection schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.969-976
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• 2007

A fuzzy classifier which needs various analyses of features using genetic algorithms is proposed. The fuzzy classifier has a simple structure, which contains a classification part based on fuzzy logic theory and a rule generation part using genetic algorithms. The rule generation part determines optimal fuzzy membership functions and inclusion or exclusion of each feature in fuzzy classification rules. We analyzed recognition rate of a specific object, then added finer features repetitively, if necessary, to the object which has large misclassification rate. And we introduce repetitive analyses method for the minimum size of string and population, and for the improvement of recognition rates. This classifier is applied to two examples of the recognition of iris data and the recognition of Thyroid Gland cancer cells. The fuzzy classifier proposed in this paper has recognition rates of 98.67% for iris data and 98.25% for Thyroid Gland cancer cells.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.491-496
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• 2002

Multi-objective Optimization Problems(MOPs) are occur more frequently than generally thought when we try to solve engineering problems. In the real world, the majority cases of optimization problems are the problems composed of several competitive objective functions. In this paper, we introduce the definition of MOPs and several approaches to solve these problems. In the introduction, established optimization algorithms based on the concept of Pareto optimal solution are introduced. And contrary these algorithms, we introduce theoretical backgrounds of Nash Genetic Algorithm(Nash GA) and Evolutionary Stable Strategy(ESS), which is the basis of Co-evolutionary algorithm proposed in this paper. In the next chapter, we introduce the definitions of MOPs and Pareto optimal solution. And the architecture of Nash GA and Co-evolutionary algorithm for solving MOPs are following. Finally from the experimental results we confirm that two algorithms based on Evolutionary Game Theory(EGT) which are Nash GA and Co-evolutionary algorithm can search optimal solutions of MOPs.

• Journal of Korean Society of Steel Construction
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• v.17 no.6 s.79
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• pp.661-672
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• 2005

For the efficient stochastic optimization of steel structures for which a large number of analyses is required, artificial neural networks,which have emerged as a powerful tool that could have been used to replace time-consuming procedures in many scientific or engineering applications, are applied. They are utilized for the solution of the equilibrium equations resulting from the application of the finite element method in connection with the reanalysis type of problem, for which a large number of finite element analyses are required in this study. As such, the use of artificial neural networks to predict finite element analysis outputs simplifies and facilitates the performance of the stochastic optimal design of structural systems where a trained neural network is used to replace the structural reanalysis phase. Moreover, to improve efficiency of used artificial neural networks, genetic algorithm is utilized. The stochastic optimizer used in this study is an algorithm based on the evolution theory. The efficiency of the proposed procedure is examined in problems with both volume (weight) functions and real-world cost functions

• Journal of Ocean Engineering and Technology
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• v.29 no.5
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• pp.366-372
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• 2015

Ocean energy is one of the most promising renewable energy resources. In particular, South Korea is one of the countries where it is economically and technically feasible to develop tidal current power plants to use tidal current energy. In this study, based on the design code for HARP_Opt (Horizontal axis rotor performance optimizer) developed by NREL (National Renewable Energy Laboratory) in the United States, and applying the BEMT (Blade element momentum theory) and GA (Genetic algorithm), the optimal shape design and performance evaluation of the horizontal axis rotor for a 200-kW-class tidal current turbine were performed using different numbers of blades (two or three) and a pitch control method (variable pitch or fixed pitch). As a result, the VSFP (Variable Speed Fixed Pitch) turbine with three blades showed the best performance. However, the performances of four different cases did not show significant differences. Hence, it is necessary when selecting the final design to consider the structural integrity related to the fatigue, along with the economic feasibility of manufacturing the blades.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.2
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• pp.66-75
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• 2010

This paper proposes an efficient method for design and implementation of the artificial intelligence (AI) of 'omok' game on the GPU. The proposed AI is designed on a cooperative structure using min-max game tree and genetic algorithm. Since the evaluation function needs intensive computation but is independently performed on a lot of candidates in the solution space, it is computed on the GPU in a massive parallel way. The implementation on NVIDIA CUDA and the experimental results show that it outperforms significantly over the CPU, in which parallel game tree and genetic algorithm on the GPU runs more than 400 times and 300 times faster than on the CPU. In the proposed cooperative AI, selective search using genetic algorithm is performed subsequently after the full search using game tree to search the solution space more efficiently as well as to avoid the thread overflow. Experimental results show that the proposed algorithm enhances the AI significantly and makes it run within the time limit given by the game's rule.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.583-590
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• 2016

This study aims to suggest a desirable dietary methods based on the oriental medicated dietary effects based on the schema of four qi and five flavors of foods originated from the yin-yang and five phase theory through clear understanding of the theory of oriental medicated dietary in a modern point of view, and through experimental analysis on the nutrient and dietary effect of 4 medicinal plants. We expect it can show us the way for promoting more healthy life styles and for preventing adult diseases by following our own medicated dietary theory. We should develop our own culinary and dietary culture which is suitable for our physical and genetic conditions for our healthy and happy lives. To successfully develop and introduce them, we should analyze their dietary effects scientifically based on both the theory of nutrialogy and the oriental medicated dietary in a modern point of view.