• 한국정밀공학회지
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• 제14권12호
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• pp.24-29
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• 1997

Recently, Genetic Algorithm(GA), which is a stochastic direct search strategy that mimics the process of genetic evolution, is widely adapted into a search procedure for structural optimization. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GA is very simple in their algorithms and there is no need of continuity of functions(or functionals) any more in GA. So, they can be easily applicable to wide area of design optimization problems. Also, owing to multi-point search procedure, they have higher porbability of convergence to global optimum compared to traditional techniques which take one-point search method. The methods consist of three genetics opera- tions named selection, crossover and mutation. In this study, a method of finding the omtimum size and topology of drilling machine is proposed by using the GA, For rapid converge to optimum, elitist survival model,roulette wheel selection with limited candidates, and multi-point shuffle cross-over method are adapted. And pseudo object function, which is the combined form of object function and penalty function, is used to include constraints into fitness function. GA shows good results of weight reducing effect and convergency in optimal design of drilling machine.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1999년도 봄 학술발표회 논문집
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• pp.102-109
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• 1999

Two phase simulated annealing algorithm is presented as a structural optimization technique and applied to minimum weight design of space trusses subjected to stress and displacement constraints under multiple loading conditions. Univariate searching algorithm is adopted for automatic selection of initial values of design variables for SA algorithm. The proper values of cooling factors and reasonable stopping criteria for optimum design of space truss structures are proposed to enhance the performance of optimization process. Optimum weights and design solutions are presented for two well-blown example structures and compared with those reported in the literature.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.110.2-110
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• 2002

Evolutionary Algorithm is the general method that can search the optimum value for the various problems. Evolutionary method consists of random selection, crossover, mutation, etc. Since the next generation is selected based on the fitness values, the crossover between chromosomes does not have any restrictions. Not only normal marriage but also consanguineous marriage will take place. In human world, consanguineous marriage was reported to cause various genetic defects, such as poor immunity about new diseases and new environment disaster, These problems translate into searching for the local optimum, not the global optimum. So, a new evolutionary algorithm is needed that prevents traps to...

• 한국공작기계학회논문집
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• 제11권5호
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• pp.15-22
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• 2002

A study is the selection of optimum forming condition for RP system. We develop the Automatic design program for machine element using visual LISP program in AutoCAD. Automatic design program reduces the required time for feedback between design and manufacturing of workpiece. Also we investigate the relationship between circularity of 3D solid model and circularity of rapid prototype using RP system and we will find optimum forming condition in RP system.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2001년도 춘계학술대회 논문집(한국공작기계학회)
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• pp.365-370
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• 2001

This study is the selection of optimum forming condition for RP system using ADS. Program using ADS reduces the required time for feedback between design and manufacturing of workpiece. When we produce rapid prototype using RP system, we investigate the relationship between Facetres in system variable number of AutoCAD and circularity of rapid prototype, and we will find optimum forming condition in RP system.

• Journal of Biosystems Engineering
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• 제18권4호
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• pp.390-401
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• 1993

This study was conducted to analyze the optimum capacity of Rice Processing Complex (RPC) and to select the optimum location of RPC based on the analysis of rice production and its commercializing rate for each county of major area of paddy field nationally. The study results showed that 500 of RPC having a drying capacity of 3,000 tons of rice would be needed nationally based on the selection analysis.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1996년도 추계학술대회 논문
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• pp.113-119
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• 1996

• 한국기계연구소 소보
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• 제4권1호
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• pp.43-50
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• 1981

• 한국지능시스템학회논문지
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• 제12권4호
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• pp.317-322
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• 2002

The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.

• Asian-Australasian Journal of Animal Sciences
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• 제25권3호
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• pp.299-303
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• 2012

Inbreeding is the mating of relatives that produce progeny having more homozygous alleles than non-inbred animals. Inbreeding increases numbers of recessive alleles, which is often associated with decreased performance known as inbreeding depression. The magnitude of inbreeding depression depends on the level of inbreeding in the animal. Level of inbreeding is expressed by the inbreeding coefficient. One breeding goal in livestock is uniform productivity while maintaining acceptable inbreeding levels, especially keeping inbreeding less than 20%. However, in closed herds without the introduction of new genetic sources high levels of inbreeding over time are unavoidable. One method that increases selection response and minimizes inbreeding is selection of individuals by weighting estimated breeding values with average relationships among individuals. Optimum genetic contribution theory (OGC) uses relationships among individuals as weighting factors. The algorithm is as follows: i) Identify the individual having the best EBV; ii) Calculate average relationships ($\bar{r_j}$) between selected and candidates; iii) Select the individual having the best EBV adjusted for average relationships using the weighting factor k, $EBV^*=EBV_j(1-k\bar{{r}_j})$ Repeat process until the number of individuals selected equals number required. The objective of this study was to compare simulated results based on OGC selection under different conditions over 30 generations. Individuals (n = 110) were generated for the base population with pseudo random numbers of N~ (0, 3), ten were assumed male, and the remainder female. Each male was mated to ten females, and every female was assumed to have 5 progeny resulting in 500 individuals in the following generation. Results showed the OGC algorithm effectively controlled inbreeding and maintained consistent increases in selection response. Difference in breeding values between selection with OGC algorithm and by EBV only was 8%, however, rate of inbreeding was controlled by 47% after 20 generation. These results indicate that the OGC algorithm can be used effectively in long-term selection programs.