• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.157-160
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• 2001

This paper has been studied a Adaptive Mutation rate Genetic Algorithm Processor. Genetic Algorithm(GA) has some control parameters such as the probability of bit mutation or the probability of crossover. These value give a priori by the designer There exists a wide variety of values for for control parameters and it is difficult to find the best choice of these values in order to optimize the behavior of a particular GA. We proposed a Adaptive mutation rate GA within a steady-state genetic algorithm in order to provide a self-adapting mutation mechanism. In this paper, the proposed a adaptive mutation rate GAP is implemented on the FPGA board with a APEX EP20K600EBC652-3 devices. The proposed a adaptive mutation rate GAP increased the speed of finding optimal solution by about 10%, and increased probability of finding the optimal solution more than the conventional GAP

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.85-92
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• 2005

In this Paper, we develope an effective web-based learning contents using interactive genetic algorithms with modified mutation operation. In the interactive genetic algorithm, reciprocal exchange mutation is used. But. we modify the mutation operator to improve the learning effects. The new web-based learning contents using interactive genetic algorithm provide the dynamic learning contents providing and real-time test system. Especially, learners can execute the interactive genetic algorithm according to the learners' characters and interests to select the efficient learning environments and contents sequences.

• Computers and Concrete
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• v.22 no.5
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• pp.493-500
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• 2018

Due to the fact that the ratio of their height to their openings is very large compared to normal beams, there are difficulties in the design and analysis of deep beams, which differ in behavior. In this study, the optimum horizontal and vertical reinforcement diameters of 5 different beams were determined by using genetic algorithms (GA) due to the openness/height ratio (L/h), loading condition and the presence of spaces in the body. In this study, the effect of different mutation operators and improved double times sensitive mutation (DTM) operator on GA's performance was investigated. In the study following random mutation (RM), boundary mutation (BM), non-uniform random mutation (NRM), Makinen, Periaux and Toivanen (MPT) mutation, power mutation (PM), polynomial mutation (PNM), and developed DTM mutation operators were applied to five deep beam problems were used to determine the minimum reinforcement diameter. The fitness values obtained using developed DTM mutation operator was higher than obtained from existing mutation operators. Moreover; obtained reinforcement weight of the deep beams using the developed DTM mutation operator lower than obtained from the existing mutation operators. As a result of the analyzes, the highest fitness value was obtained from the applied double times sensitive mutation (DTM) operator. In addition, it was found that this study, which was carried out using GAs, contributed to the solution of the problems experienced in the design of deep beams.

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.43-54
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• 2004

This study analyzes characteristics and applicability of genetic algorithms and genetic operators to optimize highway alignments. Genetic algorithms, one of artificial intelligence techniques, are fast and efficient search algorithms for generating, evaluation and finding optimal highway alignment alternatives. The performance of genetic algorithms as an optimal search tool highly depends on genetic operators that are designed as a problem-specific. This study adopts low mutation operators(uniform mutation operator, straight mutation operator, non-uniform mutation operator whole non-uniform mutation operator) to explore whole search spaces, and four crossover operators(simple crossover operator, two-point crossover operator, arithmetic crossover operator, heuristic crossover operator) to exploit food characteristics of the best chromosome in previous generations. A case study and a sensitivity analysis have shown that the eight problem-specific operators developed for optimizing highway alignments enhance the search performance of genetic algorithms, and find good solutions(highway alignment alternatives). It has been also found that a mixed and well-combined use of mutation and crossover operators is very important to balance between pre-matured solutions when employing more crossover operators and more computation time when adopting more mutation operators.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.375-378
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• 2002

In this paper we propose parallel GA to optimize mutation rate and crossover rate using server-client model. The performance of GA depend on the good choice of crossover and mutation rates. Although many researcher has been study about the good choice, it is still unsolved problem. proposed GA optimize crossover and mutation rates trough evolving subpopulation. In virtue of the server-client model, these parameters can be evolved rapidly with relatively low-grade

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.1
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• pp.52-60
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• 1997

In genetic algorithms(GA), a crossover is performed only at one or two places of a chromosome, and the fixed probabilities of crossover and mutation have been used during the entire generation. A GA with dynamic mutation is known to be superior to GAs with static mutation in performance, but so far no efficient dynamic mutation method has been presented. Accordingly in this paper, a GA is proposed to perform a uniform crossover based on the nucleotide(NU) concept, where DNA and RNA consist of NUs and also a concrete way to vary the probabilities of crossover and mutation dynamically for every generation is proposed. The efficacy of the proposed GA is demonstrated by its application to the unimodal, multimodal and nonlinear control problems, respectively. Simulation results show that in the convergence speed to the optimal value, the proposed GA was superior to existing ones, and the performance of GAs with varying probabilities of the crossover and the mutation improved as compared to GAs with fixed probabilities of the crossover and mutation. And it also shows that the NUs function as the building blocks and so the improvement of the proposed algorithm is supported by the building block hypothesis.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.12-12
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• 2017

Genetic improvement in potato can be carried out through several approaches, as sexual crosses, somatic hybridization, mutation and genetic engineering. Although the approach is different, but the goal is the same, to get a superior cultivar. Mutation and genetic engineering are very interesting methods for genetic improvement of potato plants. Mutation by gamma-ray irradiation have been performed to get some new potato cultivars which are more resistant to disease and have higher productivity. We have carried out a mutation of some potato cultivars and obtained some excellent clones to be potentially released as new superior cultivars. By the mutation method, we have released one potato cultivar for the French fries industry, and we registered one cultivar of potato for chips, and two cultivar for vegetable potatoes. Actually we are doing multi-location trial for three clones to be released as new cultivars. Through genetic engineering, several genes have been introduced into the potato plant, and we obtained several clones of transgenic potato plants. Transgenic potato plants containing FBPase gene encoding for fructose bisphosphatase, have a higher rate of photosynthesis and higher tuber productivity than non-transgenic plants. This result suggests that FBPase plays an important role in increasing the rate of photosynthesis and potato tuber productivity. Some transgenic potatoes containing the Hd3a gene are currently being evaluated for their productivity. Over expression of the Hd3a gene is expected to increase tuber productivity and induce flowering in potatoes. Transgenic potato plants containing MmPMA gene encoding for plasma membrane ATPse are more tolerant to low pH than non-transgenic plants, indicating that plasma membrane ATPase plays an important role in the potato plant tolerance to low pH stress. Transgenic potato plants containing c-lysozyme genes, are highly tolerant of bacterial wilt diseases caused by Ralstonia solanacearum and bacterial soft rot disease caused by Pectobacterium carotovorum. Expression of c-lyzozyme gene plays an important role in increasing the resistance of potato plants to bacterial diseases.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1283-1286
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• 2005

Multimodal optimization is one of the most interesting topics in evolutionary computational discipline. Simple genetic algorithm, a basic and good-performance genetic algorithm, shows bad performance on multimodal problems, taking long generation time to obtain the optimum, converging on the local extrema in early generation. In this paper, we propose a new genetic algorithm with two new genetic mutational operators, i.e. global and local mutation operators, and no genetic crossover. The proposed algorithm is similar to Simple GA and the two genetic operators are as simple as the conventional mutation. They just mutate the genes from left or right end of a chromosome till the randomly selected gene is replaced. In fact, two operators are identical with each other except for the direction where they are applied. Their roles of shaking the population (global searching) and fine tuning (local searching) make the diversity of the individuals being maintained through the entire generation. The proposed algorithm is, therefore, robust and powerful.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.512-515
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• 1997

In this paper the fuzzy-genetic controller for path-tracking of WMRs is proposed. Fuzzy controller is implemented to adaptive adjust the crossover rate and mutation rate, and genetic algorithm is implemented to adaptive adjust the control gain during the optimization. The computer simulation shows that the proposed fuzzy-genetic controller is effective.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.10
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• pp.5101-5104
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• 2012

Background: Genetic mutation is a significant factor in colon CA pathogenesis. Familial adenomatous polyposis (FAP) is an autosomal dominant hereditary disease characterized by multiple colorectal adenomatous polyps affecting a number of cases in the family. This report focuses on a family with attenuated familial adenomatous polyposis (AFAP) with exon 4 mutation, c.481C>T p.Q161X of the APC gene. Methods: We analyzed 20 members of a family with AFAP. Clinical and endoscopic data were collected for phenotype determination. Genetic analysis was also performed by direct sequencing of the APC gene. Result: Five patients with a phenotype of AFAP were found. Endoscopic polyposis was demonstrated among the second generation with genotype mutation of the disease (age > 50 years) consistent with delayed phenotypic adenomatous polyposis in AFAP. APC gene mutation was identified in exon 4 of the APC gene, with mutation points of c.481C>T p.Q161X. Laparoscopic subtotal colectomy was performed to prevent carcinogenesis. Conclusion: A family with attenuated familial adenomatous polyposis of APC related to exon 4 mutation, c.481C>T p.Q161X, was reported and the phenotypic finding was confirmed by endoscopic examination. Genetic mutation analysis might be advantageous in AFAP for long term colon cancer prevention and management due to subtle or asymptomatic phenotype presentation in early adulthood.