• The Korea Journal of Herbology
• /
• v.25 no.1
• /
• pp.1-7
• /
• 2010

Objectives : We investigated genetic toxicity of HMCO5 in relation to chromosome aberration test on Cultured Chinese Hamster Lung (CHL) in the presence and absence of S-9 mix. Methods : Experimental groups were divided into two groups: with S-9mix (+S) or without S-9 mix (-S). -S group was also divided 2 series by treatment hours (6 hr: 6-S; or 24 hr; 24-S). Each group treated with vehicle only (complete culture medium), HMCO5 (1,250, 2,500, $5,000\;{\mu}g/ml$), and cyclophosphamide monohydrate (CPA) and ethylmethanesulfonate (EMS), respectively. Results : HMC05 did not show any aberrant metaphase. However, there were significant (p < 0.01) aberrant metaphases with CPA in S+ and with EMS in S-. Conclusions : These results indicate that HMC05 formula does not show any toxicity in chromosome aberration test.

• Journal of Genetic Medicine
• /
• v.9 no.2
• /
• pp.67-72
• /
• 2012

The generation of induced pluripotent stem cells (iPSCs) derived from patients' somatic cells provides a new paradigm for studying human genetic diseases. Human iPSCs which have similar properties of human embryonic stem cells (hESCs) provide a powerful platform to recapitulate the disease-specific cell types by using various differentiation techniques. This promising technology has being realized the possibility to explore pathophysiology of many human genetic diseases at the molecular and cellular levels. Furthermore, disease-specific human iPSCs can also be used for patient-based drug screening and new drug discovery at the stage of the pre-clinical test in vitro. In this review, we summarized the concept and history of cellular reprogramming or iPSC generation and highlight recent progresses for disease modeling using patient-specific iPSCs.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.2
• /
• pp.168-175
• /
• 2003

A new algorithm based on Genetic Algorithms is proposed f3r solving process optimization problems formulated in MINLP, GDP and hybrid MINLP/GDP. This work is focused especially on the design of the Genetic Algorithm suitable to handle disjunctive programming with the same level of MINLP handling capability. Hybridization with the Simulated Annealing is experimented and many heuristics are adopted. Real and binary coded Genetic Algorithm initiates the global search in the entire search space and at every stage Simulated Annealing makes the candidates to climb up the local hills. Multi-Niche Crowding method is adopted as the multimodal function optimization technique. and the adaptation of probabilistic parameters and dynamic penalty systems are also implemented. New strategies to take the logical variables and constraints into consideration are proposed, as well. Various test problems selected from many fields of process systems engineering are tried and satisfactory results are obtained.

• Journal of Korean Institute of Industrial Engineers
• /
• v.28 no.1
• /
• pp.76-86
• /
• 2002

In this paper, we suggest a hybrid genetic algorithm reinforced by a fuzzy logic controller (flc-HGA) to overcome weaknesses of conventional genetic algorithms: the problem of parameter fine-tuning, the lack of local search ability, and the convergence speed in searching process. In the proposed flc-HGA, a fuzzy logic controller is used to adaptively regulate the fine-tuning structure of genetic algorithm (GA) parameters and a local search technique is applied to find a better solution in GA loop. In numerical examples, we apply the proposed algorithm to a simple test problem and two complex combinatorial optimization problems. Experiment results show that the proposed algorithm outperforms conventional GAs and heuristics.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.11
• /
• pp.1110-1116
• /
• 2008

In this paper, we suggest GP operators based on tree structure considering tree distributions in structure space and structural difficulties. The main idea of the proposed genetic operators is to place generated offspring into the specific region which nodes and depths are balanced and most of solutions exist. To enable that, the proposed operators are designed to utilize region information where parents belong and node/depth rates of selected subtree. To demonstrate the effectiveness of our proposed approach, experiments of binomial-3 regression, multiplexer and even parity problem are executed. The experiments results show that the proposed operators based on tree structure is superior to the results of standard GP for all three test problems in both success rate and number of evaluations.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.4
• /
• pp.339-345
• /
• 2010

This paper introduces GP(Genetic Programming) based robust corner detectors for scaled and rotated images. Various empirical algorithms have been studied to improve computational speed and accuracy including approaches, such as the Harris and SUSAN, FAST corner detectors. These techniques are highly efficient for well-defined corners, but are limited to corner-like edges which are often generated in rotated images. It is very difficult to detect correctly edges which have characteristics similar to corners. In this paper, we have focused the above challenging problem and proposed Genetic Programming-based automated generation of corner detectors which is robust to scaled and rotated images. The proposed method is compared to the existing corner detectors on test images and shows superior results.

• Proceedings of the KSME Conference
• /
• 2000.04a
• /
• pp.725-730
• /
• 2000

Genetic algorithms based on the theory of natural selection, have been applied to many different fields, and have proven to be relatively robust means to search for global optimum and handle discontinuous or even discrete data. Genetic algorithms are widely used for unconstrained optimization problems. However, their application to constrained optimization problems remains unsettled. The most prevalent technique for coping with infeasible solutions is to penalize a population member for constraint violation. But, the weighting of a penalty for a particular problem constraint is usually determined in the heuristic way. Therefore this paper proposes, the effective technique for handling constraints, the ranking penalty method and hybrid genetic algorithms. And this paper proposes dynamic mutation tate to maintain the diversity in population. The effectiveness of the proposed algorithm is tested on several test problems and results are discussed.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.3
• /
• pp.203-211
• /
• 1999

This paper presents a new approach on genetic on genetic algorithm to economic dispatch problem for valve point discontinuities. Proposed approach in this paper on genetic algorithms improves the performance to solve economic dispatch problem for valve point discontivuities through improved death penalty method, generation-apart elitism, atavism and sexual selection with sexual distinction. Numerical results on a test system consisting of 13 thermal units show that the proposed approach is faster, more robust and powerful than conventional genetic algorithms.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.2
• /
• pp.749-754
• /
• 2014

In this paper, we suggest tree-structure-aware GP (Genetic Programming) operators that heed tree distributions in structure space and their possible structural difficulties. The main idea of the proposed GP operators is to place the generated offspring of crossover and/or mutation in a specified region of tree structure space insofar as possible by biasing the tree structures of the altered subtrees, taking into account the observation that most solutions are found in that region. To demonstrate the effectiveness of the proposed approach, experiments on the binomial-3 regression, multiplexor and even parity problems are performed. The results show that the results using the proposed tree-structure-aware operators are superior to the results of standard GP for all three test problems in both success rate and number of evaluations.

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