• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.4
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• pp.184-193
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• 2008

We examine the performance of genetic algorithms using binary encoding, with respect to a change of basis. Changing the basis can result in a change in the linkage structure inherent in the fitness function. We test three simple functions with differing linkage strengths and analyze the results. Based on an empirical analysis, we show that a better basis results in a smoother fitness landscape, hence genetic algorithms based on the new encoding method provide better performance.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.263-272
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• 2024

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.3
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• pp.515-532
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• 1997

When balancing mixed model assembly lines (MMALs), workload smoothness should be considered on the model-by-model basis as well as on the station-by-station basis. This is because although station-by-station assignments may provide the equality of workload to workers, it causes the utilization of assembly lines to be inefficient due to the model sequences. This paper presents a genetic algorithm to improve the workload smoothness on both the station-by-station and the model-by-model basis in balancing MMALs. Proposed is a function by which the two kinds of workloads smoothness can be evaluated according to the various preferences of line managers. To enhance the capability of searching good solutions, our genetic algorithm puts emphasis on the utilization of problem-specific information and heuristics in the design of representation scheme and genetic operators. Experimental results show that our algorithm can provide better solutions than existing heuristics. In particular, our algorithm is outstanding on the problems with a larger number of stations or a larger number of tasks.

• Journal of Genetic Medicine
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• v.7 no.1
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• pp.16-23
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• 2010

Many endocrine disorders have a genetic component. The genetic component is the major etiologic factor in monogenic disorders, while multiple genes in conjunction with environmental and lifestyle factors contribute to the pathogenesis in complex disorders. The development of the molecular basis of inherited endocrine diseases has undergone a dramatic evolution during the last two decades. The application of molecular technology allowed us to increase our understanding of endocrine diseases, and to impact on the practice of pediatric endocrinology related to diagnosis and genetic counseling. Identification of the mutation in the particular disease by genetic testing leads to precise diagnosis in the equivocal cases and prenatal diagnosis. However, clinicians should be cautious about determining therapeutic decisions solely on the basis of molecular studies, especially in the area of prenatal diagnosis and termination of pregnancy. This review describes an introduction to molecular basis of various inherited endocrine diseases and diagnosis by genetic testing.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.279-286
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• 2005

The objective of this study is to suggest a credit scoring model of a capital company's customers by integration of multiple classifiers using genetic algorithm. For this purpose , an integrated model is derived in two phases. In first phase, three types of classifiers MLP (Multi-Layered Perceptron), RBF (Radial Basis Function) and linear models - are trained, in which each type has three ones respectively so htat we have nine classifiers totally. In second phase, genetic algorithm is applied twice for integration of classifiers. That is, after htree models are derived from each group, a final one is from these three, In result, our suggested model shows a superior accuracy to any single ones.

• Journal of Interdisciplinary Genomics
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• v.3 no.1
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• pp.13-20
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• 2021

Developmental and epileptic encephalopathies are the most devastating early-onset epilepsies, characterized by early-onset seizures that are often intractable, electroencephalographic abnormalities, developmental delay or regression, and various comorbidities. A large number of underlying genetic variants of developmental and epileptic encephalopathies have been identified over the past few decades. However, the most thorough sequencing studies leave 60-65% of patients without a molecular diagnosis. This review explores the genetic basis of developmental and epileptic encephalopathies that start within the first year of life, including Ohtahara syndrome, early myoclonic encephalopathy, epilepsy of infancy with migrating focal seizures, infantile spasms, and Dravet syndrome. The purpose of this review is to give an overview and encourage the clinicians to start considering genetic testing as an important investigation along with electroencephalogram for better understanding and management of developmental and epileptic encephalopathies.

• Proceedings of the Korean Society of Toxicology Conference
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• 2004.11a
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• pp.19-20
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• 2004

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.501-507
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• 1997

Composite material has very excellent mechanical properties including tensile stress and specific strength. Especially impact loads may be expected in many of the engineering applications of it. The suitability of composite material for such applications is determined not only by the usual paramenters, but its impactor energy-absorbing properties. Composite material under impact load has poor mechanical behavior and so needs tailoring its structure. Genetic algorithms(GA) is probabilistic optimization technique by principle of natural genetics and natural selection and neural networks(NN) is useful for prediction operation on the basis of learned data. Therefore, This study presents optimization techniques on the basis of genetic algorithms and neural networks to minimum stiffness design of laminated composite material.

• The Korean Journal of Physiology and Pharmacology
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• v.25 no.1
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• pp.1-14
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• 2021

Cardiovascular disease (CVD) accounts for approximately 30% of all deaths worldwide and its prevalence is constantly increasing despite advancements in medical treatments. Cardiac remodeling and dysfunction are independent risk factors for CVD. Recent studies have demonstrated that cardiac structure and function are genetically influenced, suggesting that understanding the genetic basis for cardiac structure and function could provide new insights into developing novel therapeutic targets for CVD. Regular exercise has long been considered a robust nontherapeutic method of treating or preventing CVD. However, recent studies also indicate that there is inter-individual variation in response to exercise. Nevertheless, the genetic basis for cardiac structure and function as well as their responses to exercise training have yet to be fully elucidated. Therefore, this review summarizes accumulated evidence supporting the genetic contribution to these traits, including findings from population-based studies and unbiased large genomic-scale studies in humans.

• Journal of Educational Research in Mathematics
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• v.11 no.2
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• pp.351-362
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• 2001

Piaget's genetic epistemology has been known as the basis of the 'New Math' and as the opposite point of view to the historico-genetic principle. But these days Piaget's theory is considered to support the historico-genetic principle so that it influences many studies. This study shows the reason of the difference of interpretations of Piaget's theory.