• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.17-24
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• 2013

Release planning in a software product line (SPL) is to select and assign the features of the multiple software products in the SPL in sequence of releases along a specified planning horizon satisfying the numerous constraints regarding technical precedence, conflicting priorities for features, and available resources. A greedy genetic algorithm is designed to solve the problems of release planning in SPL which is formulated as a precedence-constrained multiple 0-1 knapsack problem. To be guaranteed to obtain feasible solutions after the crossover and mutation operation, a greedy-like heuristic is developed as a repair operator and reflected into the genetic algorithm. The performance of the proposed solution methodology in this research is tested using a fractional factorial experimental design as well as compared with the performance of a genetic algorithm developed for the software release planning. The comparison shows that the solution approach proposed in this research yields better result than the genetic algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1728-1731
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• 2012

본 논문에서는 부모 개체의 해밍 거리에 기반하여 선택적 변이연산을 적용한 유전알고리즘을 제안한다. 유전자 형이 매우 유사한 개체들 간의 유전연산은 알고리즘의 탐색성능을 저하시키고 조기 수렴의 가능성을 증가시킨다. 본 논문에서는 이러한 현상을 극복하기 위하여, 교차연산 시 선택된 두 부모 개체간의 해밍 거리에 따라 그 값이 낮으면 교차연산 후 생성된 두 자식 개체 중 한쪽에게 높은 변이확률을 적용하고 다른 한쪽 자식은 부모와 비슷한 유전자 형으로 탐색을 계속하게 하여 조기 수렴을 방지하면서 해집단의 다양성 유지 기능을 향상 시켰다. 제안한 유전 알고리즘을 다차원 배낭 문제에 적용한 결과, 같은 조건에서 단순 유전 알고리즘(SGA) 보다 향상된 탐색 성능을 보여주었다.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.3
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• pp.48-55
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• 1999

Ths paper presents an optimization method which determines locations and size of capacitors simultaneously while minimizing power losses and improving voltage profile in radial distribution systems. Especially, the cost function associated with capacitor pla.cerrent is considered as step function due to banks of standard discrete capacities. Genetic algorithms(GA) are used to obtain efficiently the solution of the cost function associated with capacitors which is non-continuous and non-differentiable function. The strings in GA consist of the node nwnber index and size of capacitors to be installed. The length mutation operator, which is able to change the length of strings in each generation, is used. The proposed Jrethod which determines locations and size of capacitors simultaneously can reduce power losses and improve voltage proftle with capacitors of minimum size. Its efficiency is proved through the arolication in radial distribution systems.ystems.

• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.170-177
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• 2014

Genetic Algorithms are robust search and optimization techniques but have some problems such as premature convergence and convergence to local extremum. As population diversity converges to low value, the search ability decreases and converges to local extremum but population diversity converges to high value, then the search ability increases and converges to global optimum or genetic algorithm may diverge. To guarantee that genetic algorithms converge to the global optima, the genetic operators should be chosen properly. In this paper, we propose the genetic operators with the hybrid function of the average Hamming distance and the fitness value to maintain the diversity of the GA's population for escaping from the premature convergence. Results of simulation studies verified the effects of the mutation operator for maintaining diversity and the other operators for improving convergence properties as well as the feasibility of using proposed genetic operators on convergence properties to avoid premature convergence and convergence to local extremum.

• The Journal of the Korean Society for Microbiology
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• v.35 no.3
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• pp.215-224
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• 2000

In this study, the distribution of the mec regulator genes and the presence of the mutation in mecI gene and mec promoter region among 50 MRSA clinical isolates derived from a single university hospital in Korea were analyzed. Among 50 MRSA strains, 13 strains had a deletion of mecI gene, and 37 strains were found to have mutations in mecI gene or mecA promoter region corresponding to a presumptive operator of mecA, i.e., the binding site of the repressor protein. Furthermore, in order to track the evolution of methicillin-resistant Staphylococcus aureus (MRSA) distributed in Korea, we determined the MRSA clonotype by combined use of genetic organization patterns of mec regulator genes, ribotype, and coagulase type. As the result, 48 of 50 MRSA strains could be classified into four distinct clones. Clonotype I is characterized by the coagulase type 3, deletion of mecI gene, and ribotype 1 shared by NCTC10442, the first reported MRSA isolate in England (9 strains). Clonotype II is characterized by the coagulase type 4, C to T substitution at position 202 of mecI gene, and ribotypes 2, 3 and 4 shared by 85/3619 strain isolated in Austria (10 strains). Clonotype III is characterized by the coagulase type 2, mutations of mecA promoter region and/or mecI, and ribotypes 4, 5, and 6 shared by N315 strain isolated in Japan (25 strains). Clonotype IV is characterized by the coagulase type 4, deletion of mecI gene, and ribotype 7 (4 strains). The clonality of two strains could not be determined due to their undefined ribotype.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.326-354
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• 2014

In this paper, a new green clustering algorithm is proposed to be as a first approach in the framework of an energy efficient strategy for centralized enterprise high-density WLANs. Traditionally, in order to maintain the network coverage, all the APs within the WLAN have to be powered-on. Nevertheless, the new algorithm can power-off a large proportion of APs while the coverage is maintained as its always-on counterpart. The two main components of the new approach are the faster procedure based on K-means and the more accurate procedure based on Evolutionary Algorithm (EA), respectively. The two procedures are processes in parallel for different designed requirements and there is information interaction in between. In order to implement the new algorithm, EA is applied to handle the optimization of multiple objectives. Moreover, we adapt the method for selection and recombination, and then introduce a new operator for mutation. This paper also presents simulations in scenarios modeled with ray-tracing method and FDTD technique, and the results show that about 67% to 90% of energy consumption can be saved while it is able to maintain the original network coverage during periods when few users are online or the traffic load is low.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4776-4798
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• 2015

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.523-523
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• 2000

We have developed a method to build time series prediction models by Genetic Programming (GP). Our proposed CP includes two new techniques. One is the parameter optimization algorithm, and the other is the new mutation operator. In this paper, the sunspot prediction experiment by our proposed CP was performed. The sunspot prediction is good benchmark, because many researchers have predicted them with various kinds of models. We make three experiments. The first is to compare our proposed method with the conventional methods. The second is to investigate about the relation between a model-building period and prediction precision. In the first and the second experiments, the long-term data of annual sunspots are used. The third is to try the prediction using monthly sunspots. The annual sunspots are a mean of the monthly sunspots. The behaviors of the monthly sunspot cycles in tile annual sunspot data become invisible. In the long-term data of the monthly sunspots, the behavior appears and is complicated. We estimate that the monthly sunspot prediction is more difficult than the annual sunspot prediction. The usefulness of our method in time series prediction is verified by these experiments.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1009-1011
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• 1997

This paper presents an optimization method which determines locations and size of capacitors simultaneously while minimizing power losses and improving voltage profile in radial distribution systems. Especially, the cost function associated with capacitor placement is considered as step function due to banks of standard discrete capacities. Genetic algorithms(GA) are used to obtain efficiently the solution of the cost function associated with capacitors which is non-continuous and non-differentiable function. The strings in GA consist of the node number index and size of capacitors to be installed. The length mutation operator, which is able to change the length of strings in each generation, is used. The proposed method which determines locations and size of capacitors simultaneously can reduce power losses and improve' voltage profile with capacitors of minimum size. Its efficiency is proved through the application in radial distribution systems.