Browsing by Author "Kao, CY"

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A robust evolutionary algorithm for global optimization
(2002)
Yang, JM
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Lin, CJ
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Kao, CY
;
生物科技學系
;
生物資訊及系統生物研究所
This paper studies an evolutionary algorithm for global optimization. Based on family competition and adaptive rules, the proposed approach consists of global and local strategies by integrating decreasing-based mutations and self-adaptive mutations. The proposed approach is experimentally analyzed by showing that its components can integrate with one another and possess good local and global properties. Following the description of implementation details, the approach is then applied to several widely used test sets, including problems from international contests on evolutionary optimization. Numerical results indicate that the new approach performs very robustly and is competitive with other well-known evolutionary algorithms.
A robust evolutionary algorithm for training neural networks
(2001)
Yang, JM
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Kao, CY
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生物科技學系
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Department of Biological Science and Technology
A new evolutionary algorithm is introduced for training both feedforward and recurrent neural networks. The proposed approach, called the Family Competition Evolutionary Algorithm (FCEA), automatically achieves the balance of the solution quality and convergence speed by integrating multiple mutations, family competition and adaptive rides. We experimentally analyse the proposed approach by, showing that its components can cooperate with one another, and possess good local and global properties. Following the description of implementation details, our approach is then applied to several benchmark problems, including an artificial ant problem, parity problems and a two-spiral problem. Experimental results indicate that the new approach is able to stably solve these problems, and is very competitive with the comparative evolutionary algorithms.
Active pulse structural control using artificial neural networks
(2000-08-01)
Hung, SL
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Kao, CY
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Lee, JC
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土木工程學系
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Department of Civil Engineering
Increasing interest has focused on applying active control systems to civil engineering structures subjected to dynamic loading. This study presents an active pulse control algorithm, termed the adaptive neural structural active pulse (ANSAP) controller, to control civil engineering structures under dynamic loading. The ANSAP controller minimizes structural cumulative responses during earthquakes by applying active pulse control forces. The effect of pulses is assumed to be delayed until just before the next sampling time so that the control force can be calculated in time and applied; the newly developed control strategy circumvents the effect of time delay due to the computation time. The ANSAP controller also circumvents the difficulty of obtaining system parameters of a real structure for the algorithm for active structural control. Illustrative examples reveal significant reductions in cumulative structural responses, which demonstrates the feasibility of using the adaptive artificial network for controlling civil engineering structures under dynamic loading.
Adjusting the detection window to improve the soliton communication system
(2000-12-01)
Chi, S
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Kao, CY
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Dung, JC
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Wen, SF
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光電工程學系
The improvements of the Q factors of 10-Gb/s soliton systems detected by adjusting detection window are studied. We have found that the optimal width of the detection window depends on the noise-induced timing jitter, noise-induced soliton energy fluctuation, amplifier noise, dispersive wave, and soliton pulse width. (C) 2000 Elsevier Science B.V. All rights reserved.
An evolutionary algorithm for large traveling salesman problems
(2004-08-01)
Tsai, HK
;
Yang, JM
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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
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Yang, JM
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Tsai, YF
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Kao, CY
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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.
Comparative molecular binding energy analysis of HIV-1 protease inhibitors using genetic algorithm-based partial least squares method
(2004)
Chen, YC
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Yang, JM
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Tsai, CH
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Kao, CY
;
生物科技學系
Detection of structural damage via free vibration responses generated by approximating artificial neural networks
(2003-11-01)
Kao, CY
;
Hung, SL
;
土木工程學系
;
Department of Civil Engineering
This work presented a novel neural network-based approach for detecting structural damage. The proposed approach involves two steps. The first step, system identification, uses neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The second step, structural damage detection, uses the aforementioned trained NSINs to generate free vibration responses with the same initial condition or impulsive force. Comparing the periods and amplitudes of the free vibration responses of the damaged and undamaged states allows the extent of changes to be assessed. Furthermore, numerical and experimental examples demonstrate the feasibility of applying the proposed method for detecting structural damage. (C) 2003 Elsevier Ltd. All rights reserved.
Four-wave mixing between a soliton and noise in a system with large amplifier spacing
(1997-11-01)
Chi, S
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Kao, CY
;
Wen, S
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光電工程學系
;
Department of Photonics
Depletion of soliton energy by four-wave mixing between a soliton and amplifier noise in a system with 100-km amplifier spacing is studied. Dispersion exponentially decreasing fiber is used as transmission fiber. Improvement of the system by the use of a sliding-frequency filter to reduce noise power and the depletion of soliton energy is shown. The system can be further improved by compensation for depleted soliton energy. (C) 1997 Optical Society of America.
GEM: A Gaussian evolutionary method for predicting protein side-chain conformations
(2002-08-01)
Yang, JM
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Tsai, CH
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Hwang, MJ
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Tsai, HK
;
Hwang, JK
We have developed an evolutionary approach to predicting protein side-chain conformations. This approach, referred to as the Gaussian Evolutionary Method (GEM), combines both discrete and continuous global search mechanisms. The former helps speed up convergence by reducing the size of rotamer space, whereas the latter, integrating decreasing-based Gaussian mutations and self-adaptive Gaussian mutations, continuously adapts dihedrals to optimal conformations. We tested our approach on 38 proteins ranging in size from 46 to 325 residues and showed that the results were comparable to those using other methods. The average accuracies of our predictions were 80% for chi(1), 66% for chi(1+2), and 1.36 Angstrom for the root mean square deviation of side-chain positions. We found that if our scoring function was perfect, the prediction accuracy was also essentially perfect. However, perfect prediction could not be achieved if only a discrete search mechanism was applied. These results suggest that GEM is robust and can be used to examine the factors limiting the accuracy of protein side-chain prediction methods. Furthermore, it can be used to systematically evaluate and thus improve scoring functions.
GEMPLS: A new QSAR method combining generic evolutionary method and partial least squares
(2005)
Chen, YC
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Yang, JM
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Tsai, CH
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Kao, CY
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生物科技學系
We have proposed a new method for quantitative structure-activity relationship (QSAR) analysis. This tool, termed GEMPLS, combines a genetic evolutionary method with partial least squares (PLS). We designed a new genetic operator and used Mahalanobis distance to improve predicted accuracy and speed up a solution for QSAR. The number of latent variables (lv) was encoded into the chromosome of GA, instead of scanning the best lv for PLS. We applied GEMPLS on a comparative binding energy (COMBINE) analysis system of 48 inhibitors of the HIV-1 protease. Using GEMPLS, the cross-validated correlation coefficient (q(2)) is 0.9053 and external SDEP (SDEPex) is 0.61. The results indicate that GEMPLS is very comparative to GAPLS and GEMPLS is faster than GAPLS for this data set. GEMPLS yielded the QSAR models, in which selected residues are consistent with some experimental evidences.
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.
Optical coating designs using the family competition evolutionary algorithm
(2001-12-01)
Yang, JM
;
Horng, JT
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Lin, CJ
;
Kao, CY
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生物科技學系
A robust evolutionary approach, called the Family Competition Evolutionary Algorithm (FCEA), is described for the synthesis of optical thin-film designs. Based on family competition and adaptive rules, the proposed approach consists of global and local strategies by integrating decreasing mutations and self-adaptive mutations. The method is applied to three different optical coating designs with complex spectral quantities. Numerical results indicate that the proposed approach performs very robustly and is very competitive with other approaches.
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.
Structural damage detection using the optimal weights of the approximating artificial neural networks
(2002-02-01)
Hung, SL
;
Kao, CY
;
土木工程學系
;
Department of Civil Engineering
This work presents a novel neural network-based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright (C) 2001 John Wiley Sons, Ltd.