• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.87-98
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• 2002

A competitive coevolutionary algorithm is a probabilistic search method that imitates coevolution process through evolutionary arms race. The algorithm has been used to solve adversarial problems. In the algorithms, the selection of competitors is needed to evaluate the fitness of an individual. The goal of this study is to compare and analyze several competition strategies in terms of solution quality, convergence speed, balance between competitive coevolving species, population diversity, etc. With two types of test-bed problems, game problems and solution-test problems, extensive experiments are carried out. In the game problems, sampling strategies based on fitness have a risk of providing bad solutions due to evolutionary unbalance between species. On the other hand, in the solution-test problems, evolutionary unbalance does not appear in any strategies and the strategies using information about competition results are efficient in solution quality. The experimental results indicate that the tournament competition can progress an evolutionary arms race and then is successful from the viewpoint of evolutionary computation.

• IE interfaces
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• v.17 no.spc
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• pp.20-27
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• 2004

A flexible assembly system (FAS) is a production system that assembles various parts with many constraints and manufacturing flexibilities. This paper presents a new method for efficiently solving the integrated process planning and scheduling in FAS. The two problems of FAS process planning and scheduling are tightly related with each other. However, in almost all the existing researches on FAS, the two problems have been considered separately. In this research, an endosymbiotic evolutionary algorithm is adopted as methodology in order to solve the two problems simultaneously. This paper shows how to apply an endosymbiotic evolutionary algorithm to solving the integrated problem. Some evolutionary schemes are used in the algorithm to promote population diversity and search efficiency. The experimental results are reported.

• Journal of Microbiology and Biotechnology
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• v.14 no.6
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• pp.1163-1169
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• 2004

An unstructured mathematical model is presented for lactic acid fermentation based on the energy balance. The proposed model reflects the energy metabolic state and then predicts the cell growth, lactic acid production, and glucose consumption rates by relating the above rates with the energy metabolic rate. Fermentation experiments were conducted under various initial lactic acid concentrations of 0, 30, 50, 70, and 90 g/l. Also, a genetic algorithm was used for further optimization of the model parameters and included the operations of coding, initialization, hybridization, mutation, decoding, fitness calculation, selection, and reproduction exerted on individuals (or chromosomes) in a population. The simulation results showed a good fit between the model prediction and the experimental data. The genetic algorithm proved to be useful for model parameter optimization, suggesting wider applications in the field of biological engineering.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.119-124
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• 2007

The most difficult and time-intensive issue in the successful implementation of genetic algorithms is to find good parameter setting, one of the most popular subjects of current research in genetic algorithms. In this study, we present a new efficient experimental design method for parameter optimization in a genetic algorithm for general job shop scheduling problem using the Taguchi method. Four genetic parameters including the population size, the crossover rate, the mutation rate, and the stopping condition are treated as design factors. For the performance characteristic, makespan is adopted. The number of jobs, the number of operations required to be processed in each job, and the number of machines are considered as noise factors in generating various job shop environments. A robust design experiment with inner and outer orthogonal arrays is conducted by computer simulation, and the optimal parameter setting is presented which consists of a combination of the level of each design factor. The validity of the optimal parameter setting is investigated by comparing its SN ratios with those obtained by an experiment with full factorial designs.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.4
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• pp.109-124
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• 2001

This paper presents a new method that can efficiently solve the integrated problem of line balancing and model sequencing in mixed model assembly lines (MMALs). Line balancing and model sequencing are important for an efficient use of MMALs. The two problems of balancing and sequencing MMALs are tightly related with each other. However, In almost all the existing researches on mixed-model production lines, the two problems have been considered separately. In this research, an endosymbiotic evolutionary a1gorithm, which is a kind of coevolutionary a1gorithm, is adopted as a methodology in order to solve the two problems simultaneously. This paper shows how to apply an endosymbiotic evolutionary a1gorithm to solving the integrated problem. Some evolutionary schemes are used In the a1gorithm to promote population diversity and search efficiency. The proposed a1gorithm is compared with the existing evolutionary algorithms in terms of solution quality and convergence speed. The experimental results confirm the effectiveness of our approach.

• Management Science and Financial Engineering
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• v.11 no.2
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• pp.45-60
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• 2005

This paper investigates the application of the computer method COMSOAL (Computer Method of Sequencing Operations for Assembly Lines) to the process routing (PR) problem with multiple objectives. In any computer aided process planning (CAPP) system, one of the most critical activities for manufacturing a part could be to generate the sequence that optimizes production time, production cost, machine utilization or with multiple these criteria. The COMSOAL has been adopted to find the optimum sequence of operations that optimizes two major conflicting criteria : production cost and production quality. The COMSOAL is here slightly modified to simultaneously generate and evaluate a set of possible solutions (called as population) instead of processing a solution stepwise in each iteration. The significant features of the COMSOAL include : no parameters settings needed, and a guarantee of feasible solutions. Experimental results show that COMSOAL is a simple but powerful method to quickly generate multiple feasible solutions which are as good as the ones obtained from several other well-known process routing algorithms.

• Journal of Nutrition and Health
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• v.27 no.10
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• pp.988-995
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• 1994

This study was performed to investigate the influence of various dietary fiber sources(rice bran, Chinese cabbage, radish, apple, laver, sea mustard) on the intestinal microflora in rats. Eight groups of rats were fed each experimental diets containing 5% of total dietary fiber for 4 weeks. Total viable counts and the numbers of Bifidobacterium, Lactobacillus, Clostridium, E. coli and Staphylococcus were determined by nonselective medium and various selective media. The number of Bifidobacterium(p<0.01) was higher in the apple and sea mustard groups than those in the other groups and the number of Lactobacillus(p<0.01) was lower in the radish group. A decrease in the intestinal population of clostridium was shown in the sea mustard group. The E. coli(p<0.01) and Staphylococcus(p<0.01) populations decreased in the apple group compared with other groups. These findings suggest that the apple fiber and sea mustard fiber are effective in improving intestinal flora of rats.

• Clinical and Experimental Reproductive Medicine
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• v.32 no.4
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• pp.353-359
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• 2005

Heterotopic pregnancy is named when an extrauterine (ectopic) pregnancy coexists with an intrauterine pregnancy simultaneously by many causes such as PID (pelvic inflammatory disease), endometriosis, IUD (intrauterine device), previous pelvic surgery and others. This is very rare in general population, with a range of occurrence estimated between 1:7963 and 1:30000. But recently the incidence has increased as the uses of ARTs (assisted reproductive technologies) including ovulation induction, IVF-ET (in-vitro fertilization and embryo transfer) and GIFT (gamete intrafallopian transfer) increase. Because this has high maternal morbidity, mortality and fetal loss, early diagnosis and proper management is very important. We report a case of heterotopic pregnancy following IVF-ET with a brief review.

• Structural Monitoring and Maintenance
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• v.1 no.4
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• pp.427-449
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• 2014

A hybrid optimization method for the identification of state-space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.573-576
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• 2006

This paper deals with multi-objective optimization problem of finding a set of well-distributed solutions close to the true Pareto optimal solutions. In this paper, we present a two-leveled symbiotic evolutionary algorithm to efficiently solve the problem. Most of the existing multi-objective evolutionary algorithms (MOEAs) operate one population that consists of individuals representing the complete solution to the problem. The proposed algorithm maintains several populations, each of which represents a partial solution to the entire problem, and has a structure with two levels. The parallel search and the structure are intended to improve the capability of searching diverse and good solutions. The performance of the proposed algorithm is compared with those of the existing algorithms in terms of convergence and diversity. The experimental results confirm the effectiveness of the proposed algorithm.