• Computers and Concrete
• /
• v.22 no.5
• /
• pp.493-500
• /
• 2018

Due to the fact that the ratio of their height to their openings is very large compared to normal beams, there are difficulties in the design and analysis of deep beams, which differ in behavior. In this study, the optimum horizontal and vertical reinforcement diameters of 5 different beams were determined by using genetic algorithms (GA) due to the openness/height ratio (L/h), loading condition and the presence of spaces in the body. In this study, the effect of different mutation operators and improved double times sensitive mutation (DTM) operator on GA's performance was investigated. In the study following random mutation (RM), boundary mutation (BM), non-uniform random mutation (NRM), Makinen, Periaux and Toivanen (MPT) mutation, power mutation (PM), polynomial mutation (PNM), and developed DTM mutation operators were applied to five deep beam problems were used to determine the minimum reinforcement diameter. The fitness values obtained using developed DTM mutation operator was higher than obtained from existing mutation operators. Moreover; obtained reinforcement weight of the deep beams using the developed DTM mutation operator lower than obtained from the existing mutation operators. As a result of the analyzes, the highest fitness value was obtained from the applied double times sensitive mutation (DTM) operator. In addition, it was found that this study, which was carried out using GAs, contributed to the solution of the problems experienced in the design of deep beams.

• Journal of Korean Society of Transportation
• /
• v.22 no.2 s.73
• /
• pp.43-54
• /
• 2004

This study analyzes characteristics and applicability of genetic algorithms and genetic operators to optimize highway alignments. Genetic algorithms, one of artificial intelligence techniques, are fast and efficient search algorithms for generating, evaluation and finding optimal highway alignment alternatives. The performance of genetic algorithms as an optimal search tool highly depends on genetic operators that are designed as a problem-specific. This study adopts low mutation operators(uniform mutation operator, straight mutation operator, non-uniform mutation operator whole non-uniform mutation operator) to explore whole search spaces, and four crossover operators(simple crossover operator, two-point crossover operator, arithmetic crossover operator, heuristic crossover operator) to exploit food characteristics of the best chromosome in previous generations. A case study and a sensitivity analysis have shown that the eight problem-specific operators developed for optimizing highway alignments enhance the search performance of genetic algorithms, and find good solutions(highway alignment alternatives). It has been also found that a mixed and well-combined use of mutation and crossover operators is very important to balance between pre-matured solutions when employing more crossover operators and more computation time when adopting more mutation operators.

• Journal of the Korea Safety Management & Science
• /
• v.10 no.3
• /
• pp.217-225
• /
• 2008

The inventory routing problem (IRP) is an important area of Supply Chain Management. The objective function of IRP is the sum of transportation cost and inventory cost. We propose an Artificial Immune System(AIS) to solve the IRP. AIS is one of natural computing algorithm. An hyper mutation and an vaccine operator are introduced in our research. Computation results show that the hyper mutation is useful to improve the solution quality and the vaccine is useful to reduce the calculation time.

• Journal of Sericultural and Entomological Science
• /
• v.49 no.1
• /
• pp.1-7
• /
• 2007

We investigated the selection and sensitivity to environmental mutagen of silkworm reared artificial diet to develop all-year-round operation system using a specific locus mutation of Bombyx mori. In the system, mutagenicity could be detected by the egg colour manifested by the pe and/or re genes, which is a kind of recessive visible mutation of the insect. Among, hi, Backokjam, C5, and N12, varieties of silkworm, AT was higher than other varieties in eclosion rate of female, and C5 and N12 were higher in fertility of male. Bakokjam was higher in eclosion rate of female, rate of moth to lay eggs normally in female and male, no. of eggs layed in female and fertility of female. As above results, Bakokjam was finally selected as the most fitness one among varieties of silkworm reared artificial diet. But the sensitivity to mutagen was lower than other varieties. In the sensitivity to mutagen, AT was the most sensitivity to mutagen in tested varieties of silkworm. To use AT variety in this system, AT was improved major characteristics, eclosion rate, fertility, rate of moth to lay eggs normally, and so on, by crossing of other varieties including Bakokjam.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.8 no.3
• /
• pp.1-8
• /
• 1998

This paper proposes a reinforcement genetic programming based on the reinforcement learning method for the performance improvement of genetic programming. Genetic programming which has tree structure program has much flexibility of problem expression because it has no limitation in the size of chromosome compared to the other evolutionary algorithms. But worse results on the point of convergence associated with mutation and crossover operations are often due to this characteristic. Therefore the sizes of population and maximum generation are typically larger than those of the other evolutionary algorithms. This paper proposes a new method that executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. The validity of the proposed method is evaluated by appling it to the artificial ant problem.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.1
• /
• pp.37-44
• /
• 2003

In this paper, we investigated the performance of both DNA coding method and Genetic Algorithm(GA) in numeric pattern (from 0 to 9) recognition. The performance of the DNA coding method is compared to the that of the GA. GA searches effectively an optimal solution via the artificial evolution of individual group of binary string using binary coding, while DNA coding method uses four-type bases denoted by Adenine(A), Cytosine(C), Guanine(G) and Thymine(T). To compare the performance of both method, the same genetic operators(crossover and mutation) are applied and the probabilities of crossover and mutation are set the same values. The results show that the DNA coding method has better performance over GA. The reasons for this outstanding performance are multiple candidate solution presentation in one string and variable solution string length.

• Journal of Korean Association for Spatial Structures
• /
• v.5 no.3 s.17
• /
• pp.117-122
• /
• 2005

This paper investigated the optimum design of truss structures based on Genetic Algorithms (GA's). With GA's characteristic of running side by side, the overall optimization and feasible operation, the optimum design model of truss structures was established. Elite models were used to assure that the best units of the previous generation had access to the evolution of current generation. Using of non-uniformity mutation brought the obvious mutation at earlier stage and stable mutation in the later stage; this benefited the convergence of units to the best result. In addition, to avoid GA's drawback of converging to local optimization easily, by the limit value of each variable was changed respectively and the genetic operation was performed two times, so the program could work more efficiently and obtained more precise results. Finally, by simulating evolution process of nature biology of a kind self-organize, self-organize, artificial intelligence, this paper established continuous structural optimization model for ten bars cantilever truss, and obtained satisfactory result of optimum design. This paper further explained that structural optimization is practicable with GA's, and provided the theoretic basis for the GA's optimum design of structural engineering.

• Journal of Information Processing Systems
• /
• v.13 no.4
• /
• pp.1000-1013
• /
• 2017

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta-heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.11 no.2 s.54
• /
• pp.1-9
• /
• 2007

The method of generating the artificial acceleration time histories for seismic analysis based on genetic algorithms is presented. For applying to the genetic algorithms, the frequencies are selected as the decision variables eventually to be genes. An arithmetic average crossover operator and an arithmetic ratio mutation operator are suggested in this study. These operators as well as the typical simple crossover operator are utilized in generating the artificial acceleration time histories corresponding to the specified design response spectrum. Also these generated artificial time histories are checked whether their outward features are to be coincident with the recorded earthquake motion or not. The features include envelope shape, correlation condition between 2 horizontal components of motion, and the relationship of max. acceleration, max. velocity and max. displacement of ground.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.12a
• /
• pp.383-386
• /
• 2002

In this paper, we investigated the pattern recognition performance of the numeric patterns (from 0 to 9) using DNA coding method. The pattern recognition performance of the DNA coding method is compared to the that of the GA(Genetic Algorithm). GA searches effectively an optimal solution via the artificial evolution of individual group of binary string using binary coding, while DNA coding method uses four-type bases denoted by A(Adenine), C(Cytosine), G(Guanine) and T(Thymine), The pattern recognition performance of GA and DNA coding method is evaluated by using the same genetic operators(crossover and mutation) and the crossover probability and mutation probability are set the same value to the both methods. The DNA coding method has better characteristics over genetic algorithms (GA). The reasons for this outstanding performance is multiple possible solution presentation in one string and variable solution string length.