• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.432-441
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• 2000

This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Genetic algorithm has been used to identifY parameters and structure of fuzzy model because it has the ability to search optimal solution somewhat globally. The genetic algorithm, however, has a problem, which optimization process can be premature convergence in the case of lack of genetic divergence of population. Virus- evolutionary genetic algorithm(VEGA) could be a strategy against this local convergence. Therefore, we use VEGA for fuzzy modeling. In this method, local information is exchanged in population so that population can sustain genetic divergence. finally, to prove the theoretical hypothesis, we provide numerical examples to evaluate the feasibility and generality of fuzzy modeling using VEGA.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.357-364
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• 2018

This study was attempted to develop education programs through mobile apps to promote breastfeeding for high-risk mothers. The development of mobile apps was carried out in four stages, including analysis, design, implementation and evaluation, by referring to the software development life cycle. The subjects of this study were cesarean delivery mother, premature baby and twin delivery mother, and contents of education included difficulty in breastfeeding by high risk mother. Experts and users evaluated the program and found it appropriate as an educational mobile app. The education through mobile app is not limited by time and space. Therefore, it will help knowledge and continuous practice of breastfeeding by high risk mothers. It is necessary to directly test the effects of applying the breastfeeding app developed in this study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2038-2057
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• 2017

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm's optimal location, increases the learning ability of the swarm's optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1521-1524
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• 2003

In this paper, we propose a "Distance Correlation Selection operator (DCS)" as a new selection operator. For Genetic Algorithm (GA), many improvements have been proposed. The MGG (Minimal Generation Gap) model proposed by Satoh et.al. shows good performance. The MGG model has all advantages of conventional models and the ability of avoiding the premature convergence and suppressing the evolutionary stagnation. The proposed method is an extension of selection operator in the original MGG model. Generally, GA has two types of selection operators, one is "selection for reproduction", and the other is "selection for survival"; the former is for crossover and the latter is the individuals which survive to the next generation. The proposed method is an extension of the former. The proposed method utilizes distance information between individuals. From this extension, the proposed method aims to expand a search area and improve ability to search solution. The performance of the proposed method is examined with several standard test functions. The experimental results show good performance better than the original MGG model.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.376-378
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• 2004

Genetic algorithm was motivated by biological evaluation and has been applied to many industrial applications as a powerful tool for mathematical optimizations. In this paper, a new genetic optimization algorithm is proposed. The proposed method is based on Mendel's law, especially dominance and recessive property. Homologous chromosomes are introduced to implement dominance and recessive property compared with the standard genetic algorithm. Because of this property of suggested genetic algorithm, homologous chromosomes looks like the chromosomes for the standard genetic algorithm, so we can use most of existing genetic operations with little effort. This suggested method searches the larger solution area with the less probability of the premature convergence than the standard genetic algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.40
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• pp.15-22
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• 1996

Methods to find an optimal solution that is the function of the design variables satisfying all constraints have been studied, there are still many difficulties to apply them to optimal design problems. A method to solve the above difficulties is developed by using Genetic Algorithms. but, several problems that conventional GAs are ill defined are application of penalty function that can be adapted to transform a constrained optimization problem into an unconstrained one and premature convergence of solution. Thus, we developed an modified GAs to solve this problems, and two examples are given to demonstrate the effectiveness of the methodology developed in this paper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.149-152
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• 2000

This paper addresses the wavelet fuzzy modeling using Virus-Evolutionary Genetic Algorithm (VEGA). We build a fuzzy system model which is equivalent to the wavelet transform after identifying the coefficients of wavelet transform. We can obtain an accurate system model with a small number of coefficients due to the energy compaction property of the wavelet transform. It thus means that we can construct a fuzzy system model with a small number of rules. In order to identify the wide-ranged coefficients of the wavelet transform, VEGA is adopted, which has prominent ability to avoid premature local convergence that is suitable to complex optimization problems. We demonstrate the superiority of our proposed fuzzy system modeling method over the previous results by modeling nonlinear function.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.489-494
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• 1998

This paper describes a design of graphical user interface for a simulated breeding tool with multifield. The term field is used here as a population of visualized individuals that are candidates of selection. Multi-field interface enables the user to breed his/her favorite phenotypes by selection independently in each field, and he/she can copy arbitrary individual into another field. As known on genetic algorithms, a small population likely leads to premature convergence trapped by a local optimum, and migration among plural populations is useful to escape from local optimum. The multi-field user interface provides easy implementation of migration and wider diversity. We show the usefulness of multi-field user interface through an example of a breeding system of 2D CG images.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.97-97
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• 1988

Genetic Algorithms have been successfully applied to various problems (for example, engineering design problems with a mix of continuous, integer and discrete design variables) that could not have been readily solved with traditional computational techniques. But, several problems for which conventional Genetic Algorithms are ill defined are premature convergence of solution and application of exterior penalty function. Therefore, we developed an Improved Genetic Algorithms (IGAs) to solve above two problems. As a case study, IGAs is applied to several nonlinear optimization problems and it is proved that this algorithm is very useful and efficient in comparison with traditional methods and conventional Genetic Algorithm.

• IE interfaces
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• v.17 no.2
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• pp.242-248
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• 2004

Lot-streaming is the process of splitting a job (lot) into a number of smaller sublots to allow the overlapping of operations between successive machines in a multi-stage production system. A new genetic algorithm (NGA) is proposed for minimizing the mean weighted absolute deviation of job completion times from due dates when jobs are scheduled in a no-wait lot-streaming flow shop. In a no-wait flow shop, each sublot must be processed continuously from its start in the first machine to its completion in the last machine without any interruption on machines and without any waiting in between the machines. NGA replaces selection and mating operators of genetic algorithms (GAs), which often lead to premature convergence, by new operators (marriage and pregnancy operators) and adopts the idea of inter-chromosomal dominance. The performance of NGA is compared with that of GA and the results of computational experiments show that NGA works well for this type of problem.