Browsing by Author "Tsai, YF"

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An evolutionary algorithm for large traveling salesman problems
(2004-08-01)
Tsai, HK
;
Yang, JM
;
Tsai, YF
;
Kao, CY
;
生物科技學系
This work proposes an evolutionary algorithm, called the heterogeneous selection evolutionary algorithm (HeSEA), for solving large traveling salesman problems (TSP). The strengths and limitations of numerous well-known genetic operators are first analyzed, along with local search methods for TSPs from their solution qualities and mechanisms for preserving and adding edges. Based on this analysis, a new approach, HeSEA is proposed which integrates edge assembly crossover (EAX) and Lin-Kernighan (LK) local search, through family competition and heterogeneous pairing selection. This study demonstrates experimentally that EAX and LK can compensate for each other's disadvantages. Family competition and heterogeneous pairing selections are used to maintain the diversity of the population, which is especially useful for evolutionary algorithms in solving large TSPs. The proposed method was evaluated on 16 well-known TSPs in which the numbers of cities range from 318 to 13 509. Experimental results indicate that HeSEA performs well and is very competitive with other approaches. The proposed method can determine the optimum path when the number of cities is under 10 000 and the mean solution quality is within 0.0074% above the optimum for each test problem. These findings imply that the proposed method can find tours robustly with a fixed small population and a limited family competition length in reasonable time, when used to solve large TSPs.
An evolutionary approach for gene expression patterns
(2004-06-01)
Tsai, HK
;
Yang, JM
;
Tsai, YF
;
Kao, CY
;
生物資訊及系統生物研究所
This study presents an evolutionary algorithm, called a heterogeneous selection genetic algorithm (HeSGA), for analyzing the patterns of gene expression on microarray data. Microarray technologies have provided the means to monitor the expression levels of a large number of genes simultaneously. Gene clustering and gene ordering are important in analyzing a large body of microarray expression data. The proposed method simultaneously solves gene clustering and gene-ordering problems by integrating global and local search mechanisms. Clustering and ordering information is used to identify functionally related genes and to infer genetic networks from immense microarray expression data. HeSGA was tested on eight test microarray datasets, ranging in size from 147 to 6221 genes. The experimental clustering and visual results indicate that HeSGA not only ordered genes smoothly but also grouped genes with similar gene expressions. Visualized results and a new scoring function that references predefined functional categories were employed to confirm the biological interpretations of results yielded using HeSGA and other methods. These results indicate that HeSGA has potential in analyzing gene expression patterns.
Heterogeneous selection genetic algorithms for traveling salesman problems
(2003-06-01)
Tsai, HK
;
Yang, JM
;
Tsai, YF
;
Kao, CY
;
生物科技學系
This paper proposes a genetic algorithm, called the heterogeneous selection genetic algorithm (HSGA), integrating local and global strategies via family competition and edge similarity, for the traveling salesman problem (TSP). Local strategies include neighbor-join mutation and family competition, and global strategies consist of heterogeneous pairing selection and edge assembly crossover. Based on the mechanisms of preserving and adding edges, the search behaviors of neighbor-join mutation and edge assembly crossover are studied. The proposed method has been implemented and applied to 17 well-known TSPs whose numbers of cities range from 101 to 13,509. Experimental results indicate that this approach, although somewhat slower, performs very robustly and is very competitive with other approaches in the best surveys. This approach is able to find the optimum, and the average solution quality is within 0.00048 above the optima of each test problem.
Some issues of designing genetic algorithms for traveling salesman problems
(2004-11-01)
Tsai, HK
;
Yang, JM
;
Tsai, YF
;
Kao, CY
;
生物科技學系
This paper demonstrates that a robust genetic algorithm for the traveling salesman problem (TSP) should preserve and add good edges efficiently, and at the same time, maintain the population diversity well. We analyzed the strengths and limitations of several well-known genetic operators for TSPs by the experiments. To evaluate these factors, we propose a new genetic algorithm integrating two genetic operators and a heterogeneous pairing selection. The former can preserve and add good edges efficiently and the later will be able to keep the population diversity. The proposed approach was evaluated on 15 well-known TSPs whose numbers of cities range from 101 to 13509. Experimental results indicated that our approach, somewhat slower, performs very robustly and is very competitive with other approaches in our best surveys. We believe that a genetic algorithm can be a stable approach for TSPs if its operators can preserve and add edges efficiently and it maintains population diversity.