• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.203-211
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• 2016

The genetic code is a key to bio-informatics and to a science of biological self-organizing on the whole. Modern science faces the necessity of understanding and systematically explaining mysterious features of ensembles of molecular structures of the genetic code. This paper is devoted to symmetrical analysis for genetic systems. Mathematical theories of noise-immunity coding and discrete signal processing are based on Jacket matrix methods of representation and analysis of information. Both of the RNA and Jacket Matrix property also have the Element(Block) - wise Inverse Matrices. These matrix methods, which are connected closely with relations of symmetry, are borrowed for a matrix analysis of ensembles of molecular elements of the genetic code. This method is presented for its simplicity and the clarity with which it decomposes a Jacket Matrix in terms of the genetic RNA Codon.

• Journal of Science and Technology Studies
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• v.17 no.1
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• pp.71-115
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• 2017

In 1953, theoretical physicist George Gamow attempted to explain the process of protein synthesis by hypothesizing that the base sequence of DNA encodes a protein's amino acid sequence and, in response, proposed the nucleic acid-protein information transfer model, which he dubbed the "diamond code." After expressing interest in discussing the daring hypothesis, contemporary biologists, including James Watson, Francis Crick, Sydney Brenner, and Gunther Stent, were soon invited to join the RNA Tie Club, an informal research group that would also count biologists and various researchers in physics, mathematics, and computer engineering among its members. In examining the club's formation, growth, and decline in multidisciplinary research on deciphering the genetic code in the 1950s, this paper first investigates whether Gamow's idiosyncratic approach could be adopted as a collaborative research forum among contemporary biologists. Second, it explores how the RNA Tie Club's research agenda could have been expanded to other relevant research topics needing multidisciplinary approach? Third, it asks why and how the RNA Tie Club dissolved in the late 1950s. In answering those questions, this paper shows that analyses on the intersymbol correlation of the overlapping code functioned to integrate diverse approaches, including sequence decoding and statistical analysis, in research on the genetic code. As those analyses reveal, the peculiar approaches of the RNA Tie Club could be regarded as a useful method for biological research. The paper also concludes that the RNA Tie Club dissolved in the late 1950s due to the disappearance of the collaborative research agenda when the overlapping code hypothesis was abandoned.

• Proceedings of the KSR Conference
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• 2003.10b
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• pp.510-515
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• 2003

The main objective of this paper is to propose the optimal design method of welded plate girder bridges using genetic algorithm. The objective function considered is the total weight of the welded plate girder. The behavior and design constraints are formulated based on the Korean Railroad Bridge Design Code and DIC Code. Continuous design variables are used to define the cross-sectional dimensions of the member. The GAs (genetic algorithm) is used to solve the nonlinear programming problem. Several examples of minimum weight design are solved to illustrate the applicability of the proposed minimization algorithm. From the results of application examples, the optimum design of welded plate girder is successfully accomplished. Therefore, the proposed algorithm in this paper may be used efficiently and generally for the optimum design of welded plate girders.

• Structural Engineering and Mechanics
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• v.10 no.4
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• pp.313-336
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• 2000

The focus of this paper is on the development and implementation of a methodology for automated design of discrete structural systems. The research is aimed at utilizing Genetic Algorithms (GA) as an automated design tool. Several key enhancements are made to the simple GA in order to increase the efficiency, reliability and accuracy of the methodology for code-based design of structures. The AISC-ASD design code is used to illustrate the design methodology. Small as well as large-scale problems are solved. Simultaneous sizing, shape and topology optimal designs of structural framed systems subjected to static and dynamic loads are considered. Comparisons with results from prior publications and solution to new problems show that the enhancements made to the GA do indeed make the design system more efficient and robust.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6A
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• pp.369-378
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• 2012

In this study, direct-search based optimization methods are applied for blade shape optimization of wind turbines and the optimization performances of several methods including conventional genetic algorithm, micro genetic algorithm and pattern search method are compared to propose a more efficient method. For this purpose, the currently available version of HARP_Opt (Horizontal Axis Rotor Performance Optimizer) code is enhanced to rationally evaluate the annual energy production value according to control strategies and to optimize the blade shape using pattern search method as well as genetic algorithm. The enhanced HARP_Opt code is applied to obtain the optimal turbine blade shape for 1MW class wind turbines. The results from pattern search method are compared with the results from conventional genetic algorithm and also micro genetic algorithm and it is found that the pattern search method has a better performance in achieving higher annual energy production and consistent optimal shapes and the micro genetic algorithm is better for reducing the calculation time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.41
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• pp.1-14
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• 1997

This study proposes a genetic algorithm using Pr(equation omitted)fer number for the traveling salesman problem(PNGATSP). Nearest neighbor nodes are mixed with randomly selected nodes at the stage of generating initial solutions. Proposed PNGATSP adopts a few ideas which are different from traditional genetic algorithms. For instance, an exponential fitness function and elitism are used and Pr(equation omitted)fer number is used for encoding TSP. Genetic operators are selected by experiments, which make a good solution among four combinations of conventional genetic operators and new genetic operators. For respective combinations, robust set of parameters is determined by the experimental designing approach. The feature of Pr(equation omitted)fer number code for TSP and the search power of GA using Pr(equation omitted)fer number is analysed. The best is a combination of OX(order crossover) and swap, which is superior to the other experimented combinations of genetic operators by 1.0%∼12.8% deviation.

• Computers and Concrete
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• v.23 no.4
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• pp.255-265
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• 2019

The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams' laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams' laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes.

• Nuclear Engineering and Technology
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• v.44 no.3
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• pp.273-282
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• 2012

A new effective methodology for optimizing the enrichment of low-enriched zones as well as gadolinia fuel ($UO_2/Gd_2O_3$) rod designs in PLUS7 fuel assemblies was developed to minimize the maximum peak power in the core and to maximize the cycle lifetime. An automated link code was developed to integrate the genetic algorithm (GA) and the core design code package of ALPHA/PHOENIX-P/ANC and to generate and evaluate the candidates to be optimized efficiently through the integrated code package. This study introduces an optimization technique for the optimization of gadolinia fuel rod designs in order to effectively reduce the peak powers for a few hot assemblies simultaneously during the cycle. Coupled with the gadolinia optimization, the optimum enrichments were determined using the same automated code package. Applying this technique to the reference core of Ulchin Unit 4 Cycle 11, the gadolinia fuel rods in each hot assembly were optimized to different numbers and positions from their original designs, and the maximum peak power was decreased by 2.5%, while the independent optimization technique showed a decrease of 1.6% for the same fuel assembly. The lower enrichments at the fuel rods adjacent to the corner gap (CG), guide tube (GT), and instrumentation tube (IT) were optimized from the current 4.1, 4.1, 4.1 w/o to 4.65, 4.2, 4.2 w/o. The increase in the cycle lifetime achieved through this methodology was 5 effective full-power days (EFPD) on an ideal equilibrium cycle basis while keeping the peak power as low as 2.3% compared with the original design.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.79-85
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• 2007

In this paper, a real-coded genetic algorithm is proposed for identification of time delay systems from step responses. FOPDT(First-Order Plus Dead-Time) and SOPDT(Second-Order Plus Dead-Time) systems, which are the most useful processes in this field, but are difficult for system identification because of a long dead-time problem and a model mismatch problem. Genetic algorithms have been successfully applied to a variety of complex optimization problems where other techniques have often failed. Thus, the modified crossover operator of a real-code genetic algorithm is proposed to effectively search the system parameters. The proposed method, using a real-coding genetic algorithm, shows better performance characteristics when compared to the usual area-based identification method and the directed identification method that uses step responses.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.11
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• pp.117-126
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• 2003

GA(genetic algorithm) has a powerful searching ability and is comparatively easy to use and to apply as well. By that reason, GA is in the spotlight these days as an optimization skill for mechanical systems.$^1$However, GA has a low efficiency caused by a huge amount of repetitive computation and an inefficiency that GA meanders near the optimum. It also can be shown a phenomenon such as genetic drifting which converges to a wrong solution.$^{8}$ These defects are the reasons why GA is not widdy applied to real world problems. However, the low efficiency problem and the meandering problem of GA can be overcomed by introducing parallel computation$^{7}$ and gray code$^4$, respectively. Standard GA(SGA)$^{9}$ works fine on small to medium scale problems. However, SGA done not work well for large-scale problems. Large-scale problems with more than 500-bit of sere's have never been tested and published in papers. In the result of using the SGA, the powerful searching ability of SGA doesn't have no effect on optimizing the problem that has 96 design valuables and 1536 bits of gene's length. So it converges to a solution which is not considered as a global optimum. Therefore, this study proposes ExpGA(experience GA) which is a new genetic algorithm made by applying a new probability parameter called by the experience value. Furthermore, this study finds the solution throughout the whole field searching, with applying ExpGA which is a optimization technique for the structure having genetic drifting by the standard GA and not making a optimization close to the best fitted value. In addition to them, this study also makes a research about the possibility of GA as a optimization technique of large-scale design variable problems.