• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.201-212
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• 2007

Genetic algorithms (GAs) are very useful methods for global search and have been applied to various optimization problems. They have two kinds of important search mechanisms, crossover and mutation. Because the performance of GAs depends on these operators, a large number of operators have been developed for improving the performance of GAs. Especially, many researchers have been more interested in a crossover operator than a mutation operator. The reason is that a crossover operator is a main search operator in GAs and it has a more effect on the search performance. So, we also focus on a crossover operator. In this paper we first investigate the drawback of various crossovers, especially subtour-based crossovers and then introduce a new crossover operator to avoid such drawback and to increase efficiency. Also we compare it with several crossover operators for symmetric traveling salesman problem (STSP) for showing the performance of the proposed crossover. Finally, we introduce an efficient simple hybrid genetic algorithm using the proposed operator and then the quality and efficiency of the obtained results are discussed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1636-1641
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• 2008

In this study, we introduce a new approach to Parallel Genetic Algorithms (PGA) which combines AHFCGA with crossover operator. As to crossover operators, we use three types of the crossover operators such as modified simple crossover(MSX), arithmetic crossover(AX), and Unimodal Normal Distribution Crossover(UNDX) for real coding. The AHFC model is given as an extended and adaptive version of HFC for parameter optimization. The migration topology of AHFC is composed of sub-populations(demes), the admission threshold levels, and admission buffer for the deme of each threshold level through succesive evolution process. In particular, UNDX is mean-centric crossover operator using multiple parents, and generates offsprings obeying a normal distribution around the center of parents. By using test functions having multimodality and/or epistasis, which are commonly used in the study of function parameter optimization, Experimental results show that AHFCGA can produce more preferable output performance result when compared to HFCGA and RCGA.

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

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.332-334
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• 1998

Maintenance scheduling is one of mid-term scheduling problems of power systems. There have been many methods for this problem, but there is no effective way to treat all the generators simultaneously except evolutionary algorithms. In this paper, we apply GA to the maintenance scheduling problem. And we proposed new crossover operator(BOX type crossover) to improve searching ability of GA. Satisfactory results are obtained by GA with proposed crossover operator.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.3
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• pp.15-34
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• 1994

This paper considers the genetic algorithms(GAs) for the mixed model assembly line sequencing(MMALS) in which the objective is to minimize the overall line length. To apply the GAs to the MMALS, the representation, selection, genetic sequencing operators, and genetic parameters are studied. Especially, the existing sequencing binary operators such as partially map crossover(PMX), cycle crossover(CX), and order crossover (OX) are modified to be suitable for the MMALS, and a new sequencing binary operator called immediate successor relationship crossover (ISR) is introduced. These binary operators mentioned above and/or unary operators such as swap, insertion, inversion, displacement, and splice are compared to find operators which work well in the MMALS. Experimental results indicate that 1) among the binary operators ISR operator is the best, followed by the modified OX, and the modified PMX, with the modified CX being the worst, 2) among the unary operators inversion operator is the best, followed by displacement, swap, and insertion, with splice being the worst, and 3) in general, the unary operators perform better than the binary operators for the MMALS.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.203-205
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• 1999

The unit commitment is an important problem of production scheduling which determines the generating unit to in service(on/off) during scheduling period, to meet system demand and reserve requirement at minimum cost. This paper presents an box type crossover to improve searching ability of GA, to solve unit commitment problem. Satisfactory results are obtained by GA with the proposed crossover operator.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.341-349
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• 2013

This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved genetic algorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the genetic algorithm, of crossover and mutation, are revisited, and a new rank-based multi-parent crossover operator is utilized. The rank-based crossover operator simultaneously enhances both the local, and the global exploration. The benchmark results demonstrate remarkable improvements, in terms of efficiency and robustness, as compared to other state-of-the-art algorithms. The developed algorithm is adopted for two baseline flexbeam section designs, and optimum cross-section configurations are obtained with less function evaluations, and less computation time.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.217-222
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• 2011

Genetic algorithms are one of the most important methods used to solve the Traveling Salesman Problem. Therefore, many researchers have tried to improve the Genetic Algorithm by using different methods and operations in order to find the optimal solution within reasonable time. This paper intends to find a new approach that adopts an improved genetic algorithm to solve the Traveling Salesman Problem, and compare with the well known heuristic method, namely, Kohonen Self-Organizing Map by using different data sets of symmetric TSP from TSPLIB. In order to improve the search process for the optimal solution, the proposed approach consists of three strategies: two separate tour segments sets, the improved crossover operator, and the improved mutation operator. The two separate tour segments sets are construction heuristic which produces tour of the first generation with low cost. The improved crossover operator finds the candidate fine tour segments in parents and preserves them for descendants. The mutation operator is an operator which can optimize a chromosome with mutation successfully by altering the mutation probability dynamically. The two improved operators can be used to avoid the premature convergence. Simulation experiments are executed to investigate the quality of the solution and convergence speed by using a representative set of test problems taken from TSPLIB. The results of a comparison between the new approach using the improved genetic algorithm and the Kohonen Self-Organizing Map show that the new approach yields better results for problems up to 200 cities.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.450-464
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• 2023

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.

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