改良式快速基因演算法: 工程與管理之應用

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陳宗堯(Zong-Yao Chen) 查詢紙本館藏

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資訊管理學系

論文名稱

改良式快速基因演算法: 工程與管理之應用
(Efficient-Genetic Algorithm for Engineering and Management Applications)

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摘要(中)

進化式演算法早已經受到廣泛的應用，這類方法擁有的特性非常適合用來處理一些問題，例如黑箱與NP-hard (Non-deterministic Polynomial-time hard)的問題.
然而可惜的是，這類方法已經被證實屬於耗時(time consuming)的方法，這與實務上的需求是有差異的，例如: 工廠不可能花費一整天的時間等待一個最佳的排程、企業不可能耗費1個月進行資料的精簡。因此，縮短這類演算法的運算時間是極為重要的。
本研究提出了一個新穎的最佳化演算法名為快速基因演算法(Efficient-Genetic Algorithm, EGA), 透過增加更多的生物學的原理來改善演算法的演化效率。換句話說，在有限的資源下，生物經過了長時間的演化，找出了最有效率的演化以及存活方法，這是值得參考的。
本文透過兩個主要的實驗來驗證EGA的有效性，在第一個實驗中，針對EGA測試了兩個常見的最佳化問題。此外，本研究也納入了兩個知名的演算法分別是基因演算法(Genetic algorithm, GA)以及其改良的變種版本免疫演算法(Immunity algorithm, IA)。然而在第二個實驗中，為了貼近實務的需求，本研究採用了四個高維度的資料集進行資料縮減的實驗，並與四個知名的方法進行比較，四種方法分別為IB3、DROP3、ICF 還有GA。

摘要(英)

Evolutionary computations have been widely used in many real word problems. In particular, evolutionary algorithms can be considered as the global optimization methods with a met heuristic or stochastic optimization character and they are widely applied for black box problems (no derivatives known) and non-deterministic polynomial-time hard problems (NP-hard), often in the context of expensive optimization. However, their computational complexities are very high leading to the major limitation in practice.
In this dissertation, we introduce a novel Efficient-Genetic Algorithm (EGA), which fits “biological evolution” into the evolutionary process. In other words, after long-term evolution, individuals find the most efficient way to allocate resources and evolve.
There are two experiments to validate the EGA. The first experimental study is based on a scheduling problem, and two state-of-the-art algorithms including Genetic algorithm (GA) and Immunity algorithm (IA) are compared with EGA. The second one focuses on the data reduction problem where four very high dimensional datasets are used. In addition, four state-of-the-art algorithms including IB3, DROP3, ICF, and GA are compared with EGA.

關鍵字(中)

★ 資料縮減
★ 作業排程
★ 資料探勘
★ 機器學習
★ 人工智慧
★ 基因演算法
★ 高維度資料

關鍵字(英)

★ data mining
★ data reduction
★ genetic algorithms
★ high dimensional data
★ machine learning
★ scheduling problems

論文目次

中文提要……………………………………………………………………………………………………… i
英文提要……………………………………………………………………………………………………… ii
誌謝………………………………………………………………………………………………………………… iii
目錄……………………………………………………………………………………………………………… iv
表目錄…………………………………………………………………………………………………………… v
圖目錄………………………………………………………………………………………………………… vii
符號說明……………………………………………………………………………………………………… viii
1.Introduction…………………………………………………………………………………… - 1 -
1.1 Background Evolutionary Computation………………… - 2 -
1.2 Problem Defined……………………………………………………………………… - 3 -
1.3 Differences and Contributions………………………………… - 4 -
1.4 Dissertation Architecture…………………………………………… - 5 -
2. Literature Review…………………………………………………………………… - 6 -
2.1 Evolutionary Algorithms………………………………………………… - 6 -
2.2 The Genetic Algorithms…………………………………………………… - 9 -
I. Applications:……………………………………………………………………… - 9 -
II. Improvement:……………………………………………………………………… - 9 -
2.2.1 Improvement – Encoding……………………………………………… - 9 -
2.2.2 Improvement – Parameter Optimization………… - 10 -
2.2.3 Improvement - Operation Mode/flow………………… - 10 -
2.2.4 Summary of Genetic Algorithms…………………………… - 11 -
2.2.5 Application of GA in Surface Mount Technology (SMT)………………………………………………………………………………………………………………………… - 12 -
2.2.2 Application of GA in Data Reduction…………… - 13 -
2.3 Problem Background……………………………………………………………… - 14 -
2.3.1 Data Reduction…………………………………………………………………… - 14 -
2.3.2 Operations Scheduling………………………………………………… - 15 -
3. The Efficient Genetic Algorithm……………………………… - 17 -
3.1 The Basic Concept………………………………………………………………… - 17 -
3.2 Novel Features of EGA……………………………………………………… - 18 -
3.2.1 Reasonable Convergence……………………………………………… - 18 -
3.2.2 Nonlinear Adaptability……………………………………………… - 20 -
3.2.3 Genetic King – Inter-Generational Mating - 22 -
3.2.4 Hardy-Weinberg law………………………………………………………… - 23 -
3.2.5 Great Migration………………………………………………………………… - 24 -
3.2.6 New Generation…………………………………………………………………… - 25 -
3.3 A Small Running Example of Data Reduction Problem……………………………………………………………………………………………………… - 25 -
3.4 Pilot Test…………………………………………………………………………………… - 31 -
3.4.1 Mathematical Optimization……………………………………… - 31 -
3.4.2 Small Scale Data Reduction Problem……………… - 36 -
3.5 Discussion…………………………………………………………………………………… - 42 -
3.5.1 Evaluation and New Generation…………………………… - 42 -
3.5.2 Selection of Kings and Mating…………………………… - 43 -
3.5.3 Great Migration………………………………………………………………… - 45 -
3.5.4 Time Complexity Analysis………………………………………… - 46 -
4. Experiments…………………………………………………………………………………… - 48 -
4.1 Experiments I: High Dimensional Data Reduction………………………………………………………………………………………………… - 48 -
4.1.1 Classification Accuracy…………………………………………… - 49 -
4.1.2 Storage Requirements…………………………………………………… - 51 -
4.1.3 Computational Cost………………………………………………………… - 52 -
4.1.4 Discussion of High Dimensional Data Reduction………………………………………………………………………………………………… - 53 -
4.2 Experiments II: Scheduling Problem…………………… - 55 -
4.2.1 Moving the placement machine:…………………………… - 57 -
4.2.2 Switch machine:………………………………………………………………… - 57 -
4.2.3 Experimental setup………………………………………………………… - 58 -
4.2.4 Experimental result……………………………………………………… - 59 -
4.2.5 Discussion of Scheduling Problem…………………… - 61 -
5. Conclusion……………………………………………………………………………………… - 62 -
Referance………………………………………………………………………………………………… - 65 -
Appendix 1 - Pseudo code of EGA……………………………………… - 74 -

參考文獻

〔1〕 D. Agrawal, P. Bernstein, E. Bertino, S. Davidson, U. Dayal, M. Franklin, J. Gehrke, L. Haas, A. Halevy, J. Han, H. V. Jagadish, A. Labrinidis, S. Madden, Y. Papakonstantinou, J. M. Patel, R. Ramakrishnan, K. Ross, C. Shahabi, D. Suciu, S. Vaithyanathan, and J. Widom. : Challenges and Opportunities with Big Data -- A community white paper developed by leading researchers across the United States. http://cra.org/ccc/docs/init/bigdatawhitepaper.pdf, Dec. 26, 2013.
〔2〕 “1965 – "Moore′s Law" Predicts the Future of Integrated Circuits”. Computer History Museum. 2007. Retrieved 2009-03-19.
〔3〕 N. Wirth. “A Plea for Lean Software,” Computer, Vol. 28(2), pp. 64–68, February 1995.
〔4〕 PE. Ross. “5 Commandments,” IEEE Spectrum, Vol. 40(12), pp. 30-35, December 2003.
〔5〕 D. Poole, A. Mackworth and R. Goebel, Computational Intelligence: A Logical Approach, Oxford University Press, New York. 1998.
〔6〕 SJ. Russell, P. Norvig, Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, 2003.
〔7〕 N. Nilsson, Artificial Intelligence: A New Synthesis., Morgan Kaufmann Publishers, 1998.
〔8〕 Y.C. Hsieh, Y.C. Lee, P.S. You and T.C. Chen, “An effective immune based approach for the no-wait flow shop scheduling problems with multiple machines,” Advanced Materials Research, Vol. 97-101, 2010.
〔9〕 C.F. Tsai, Z.Y. Chen and S.W. Ke, “Evolutionary Instance Selection for Text Classification,” Journal of Systems and Software, 2014.
〔10〕 Z. Michalewicz, Genetic Algorithms Plus Data Structures Equals Evolution Programs 2nd, Springer-Verlag New York, Inc. Secaucus, NJ, USA. 1994.
〔11〕 T. Back, D. B. Fogel and Z. Michalewicz, Handbook of Evolutionary Computation, IOP Publishing Ltd. Bristol, UK, 1997.
〔12〕 I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, Vol. 3, pp. 1157–1182, 2003.
〔13〕 P. Ball, “Natural strategies for the molecular engineer,” Nanotechnology, Vol.13, pp. 15-28, 2002.
〔14〕 M. Srinivas and L.M. Patnaik, “Genetic algorithms: a survey” Computer, Vol.27(6), pp. 17-26, June 1994.
〔15〕 D. Bratton, and J. Kennedy, “Deﬁning a Standard for Particle Swarm Optimization,” IEEE Swarm Intelligence Symposium, pp. 120 - 127, April 2007.
〔16〕 D.H. Wolpert and W.G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, Vol. 1(1) pp.67-82, April 1997.
〔17〕 R. Hassan, B. Cohanim and O. Weck “A comparison of particle swarm optimization and the genetic algorithm,” 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, pp. 1-13, 2005.
〔18〕 I. Kecskes, L. Szekacs, J.C. Fodor and P. Odry, “PSO and GA Optimization Methods Comparison on Simulation Model of a Real Hexapod Robot,” IEEE 9th International Conference on Computational Cybernetics, pp. 125-130, 2013.
〔19〕 N. Shafiabady, M. Teshnehlab and M. Aliyari Shooredeh, “A Comparison of PSO and GA Combined with LS and RLS in Identification Using Fuzzy Gaussian Neural Networks,” IEEE International Symposium on Industrial Electronics, 2009. ISIE 2009, pp. 2081-2086, 2009.
〔20〕 C. Ou and W. Lin, “Comparison between PSO and GA for Parameters Optimization of PID Controller,” IEEE International Conference on Mechatronics and Automation, pp. 2471-2475, June 2006.
〔21〕 S. Panda and N. P. Padhy, “Comparison of Particle Swarm Optimization and Genetic Algorithm for TCSC-based Controller Design,” Journal Applied Soft Computing, Vol.8(4) pp. 1418-1427, September 2008.
〔22〕 Y. Maldonado and O. Castillo, “Comparison between Multiobjective GA and PSO for Parameter Optimization of AT2-FLC for a real application in FPGA,” Fuzzy Information Processing Society (NAFIPS), 2012 Annual Meeting of the North American, pp. 1-6, August 2012.
〔23〕 S. Mahmoudi, B.S. Lahijan and H.R. Kanan, “ANFIS-Based Wrapper Model Gene Selection for Cancer Classification on Microarray Gene Expression Data,” 13th Iranian Conference on Fuzzy Systems (IFSC), pp. 1-6, August 2013.
〔24〕 S. Debnath and R.N. Ray, “Harmonic Elimination in Multilevel Inverter using GA and PSO:A Comparison,” IEEE Students′ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1-5, March 2012.
〔25〕 S. Soares, C. H. Antunes and R. Araújo, “Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development,” Neurocomputing, Vol. 121, pp. 498-511, December 2013.
〔26〕 S. N. Kumbharana and G. M. Pandey, “A Comparative Study of ACO, GA and SA for Solving Travelling Salesman Problem,” International Journal of Societal Applications of Computer Science, Vol. 2(2), pp.224-228, 2013.
〔27〕 J.F. Schutte, B.I. Koh, J.A. Reinbolt, R.T. Haftka, A.D. George and B.J. Fregly, “Evaluation of a Particle Swarm Algorithm For Biomechanical Optimization,” Journal of biomechanical engineering, Vol. 127(3), pp. 465-474, June 2005.
〔28〕 J. Jerald, P. Asokan, G. Prabaharan and R. Saravanan, “Scheduling optimisation of ﬂexible manufacturing systems using particle swarm optimisation algorithm,” International Journal of Advanced Manufacturing Technology, Vol.25 (9-10), pp.964-971, May 2005.
〔29〕 K. Ishaquea, Z. Salama, S. Mekhilefc and A. Shamsudina, “Parameter extraction of solar photovoltaic modules using penalty-based differential evolution,” Applied Energy, Vol. 99, pp. 297–308, November 2012.
〔30〕 S.A. Ethni, B. Zahawi, D. Giaouris and P.P. Acarnley, “Comparison of Particle Swarm and Simulated Annealing Algorithms for Induction Motor Fault Identification,” 7th IEEE International Conference on Industrial Informatics, pp. 470-474, June 2009.
〔31〕 K.O. Jones, “Comparison of genetic algorithm and particle swarm Optimisation,” International Conference on Computer Systems and Technologies, 2005.
〔32〕 D. E. Goldberg, Gnetic Algorithms in search, optimization and machine learning, Addison Wesley. 1989.
〔33〕 J. Horn, N. Nafpliotis and D.E. Goldberg, “A Niched Pareto Genetic Algorithm for Multiobjective Optimization,” International Conference on Evolutionary Computation, pp.82-87, 1994.
〔34〕 K. Deb, S. Agrawal, A. Pratap and T. Meyarivan, “A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II,” IEEE Transactions on evolutionary computation, Vol. 6(2), April 2002.
〔35〕 Y. C. Hsieh, T.-C. Chen and D.L. Bricker, “Genetic algorithms for reliability design problems,” Microelectronics and Reliability, Vol. 38(10), 1599-1605, October 1998.
〔36〕 C. L. Huang and, C. J. Wang, “A GA-based Feature Selection and Parameters Optimization for Support Vector Machines,” Expert Systems with Application, Vol.31(2), pp.231-240, 2006.
〔37〕 S. Lessmann, R. Stahlbock, S. F. Crone, “Genetic Algorithms for Support Vector Machine Model Selection,” Proc. of the Intern. Joint Conf. on Neural Networks (IJCNN′06), pp. 3063-3069, 2006.
〔38〕 C. F. Tsai, Y. C. Hsiao, “Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches,” Decision Support Systems, Vol. 50(1), pp. 258-269. Dec, 2010.
〔39〕 M. L. Raymer, W. F. Punch, E. D. Goodman, L. A. Kuhn, A. K. Jain, “Dimensionality reduction using genetic algorithms,” IEEE Transactions on Evolutionary Computation, Vol. 4, pp. 164-171, 2000.
〔40〕 S. S. Choi and B.R. Moon, “Normalization for genetic algorithms with nonsynonymously redundant encodings,” IEEE Transactions on Evolutionary Computation, Vol. 12 (5), pp. 604–616, 2008.
〔41〕 E. Thompson, T. Paulden and D. K.. Smith, “The Dandelion Code: A new coding of spanning trees for genetic algorithms,” IEEE Transactions on Evolutionary Computation, Vol. 11(1), pp 91-100, 2007.
〔42〕 J. Cervantes and C.R. Stephens, “Limitations of Existing Mutation Rate Heuristics and How a Rank GA Overcomes Them,” IEEE Transactions on Evolutionary Computation, 2009, pp.369-397.
〔43〕 G. R. Harik and F. G. Lobo, “A parameter-less genetic algorithm,” Proceedings of the Genetic and Evolutionary Computation Conference, Vol. 1, pp. 258–265, 1999.
〔44〕 V. Togan, A.T. Daloglu, “An improved genetic algorithm with initial population strategy and self-adaptive member grouping,” Computers and Structures, Vol. 86(11-12), pp. 1204-1218, 2008.
〔45〕 T. Park and K. R. Ryu , “A Dual-Population Genetic Algorithm for Adaptive Diversity Control,” IEEE Transactions on Evolutionary Computation, Vol.14(6), pp. 865-884, December 2010.
〔46〕 J. Yao, N. Kharma, and P. Grogono, “Bi-objective Multi Population Genetic Algorithm for Multimodal Function Optimization,” IEEE Transactions on Evolutionary Computation, Vol. 14 (1), February, 2010.
〔47〕 A. Munawar, M. Wahib, M. Munetomo and K. Akama, “A Survey: Genetic Algorithms and the Fast Evolving World of Parallel Computing,” The 2008 IEEE International Conference on High Performance Computing and Communications, pp. 897-902, 2008.
〔48〕 D.D. Lim, Y.S. Ong, Y.C. Jin, B. Sendho and B.S. Lee, “Efficient hierarchical parallel genetic algorithms using grid computing,” Future Generation Computer Systems, Vol. 23 (4), pp. 658-670, 2007.
〔49〕 M. Ayob, G. Kendall, “A triple objective function with a Chebychev dynamic pick-and-place point specification approach to optimize the surface mount placement machine,” European Journal of Operational Research, Vol. 164(3), pp. 609-626, 2005.
〔50〕 D. Golding, “PCB Assembly,” Assembly Automation, Vol. 15 (2), pp. 10–13, 1995.
〔51〕 K.P. Ellis, F.J. Vittes, J.E. Kobza, Optimizing the performance of a surface mount placement machine, IEEE Transactions on Electronic Packaging Manufacturing, Vol. 24 (3), pp. 160–170, 2001.
〔52〕 Y. Crama, J. van de Klundert, F.C.R. “Spieksma, Production planning problems in printed circuit board assembly,” Discrete Applied Mathematics, Vol. 123 (1–3), pp. 339–361, 2002.
〔53〕 K. Feldmann, J. Sturm, “Closed loop quality control in printed circuit assembly,” IEEE Transactions on Components, Packaging, and Manufacturing Technology, Part A. Vol. 17(2), pp.270-276, 1994.
〔54〕 T.M. Tirpak, P.C. Nelson, A.J. Aswani, “Optimization of revolver head SMT machines using adaptive simulated annealing (ASA),” Electronics Manufacturing Technology Symposium, 2000. Twenty-Sixth IEEE/CPMT International, pp. 214 –220, 2000.
〔55〕 P.J. Egbelu, C. Wu, R. Pilgaonkar, “Robotic assembly of printed circuit boards with component feeder location consideration,” Production Planning and Control, Vol. 7, pp. 162–175, 1996.
〔56〕 S. Pradhan, X. Wu, “Instance selection in data mining,” Technical Report, Department of Computer Science, University of Colorado at Boulder, 1999.
〔57〕 N. Jankowski, M. Grochowski, “Comparison of instances selection algorithms I: algorithms survey,” Proceedings of the International Conference on Artificial Intelligence and Soft Computing, pp. 598-603, 2004.
〔58〕 D.R. Wilson and T.R. Martinez, “Reduction techniques for instance-based learning algorithms,” Machine Learning, Vol. 38, pp. 257-286, 2000.
〔59〕 H. Brighton, C. Mellish, “Advances in instance selection for instance-based learning algorithms,” Data Mining and Knowledge Discovery, Vol. 6, pp. 153-172, 2002.
〔60〕 J. Derrac, S. García and F. Herrera, “A survey on evolutionary instance selection and generation,” International Journal of Applied Metaheuristic Computing, Vol. 1(1), pp. 60-92, 2010.
〔61〕 J.R. Cano, F. Herrera, M. Lozano, “Using evolutionary algorithms as instance selection for data reduction: an experimental study,” IEEE Transactions on Evolutionary Computation Vol. 7(6), pp. 561-575, 2003.
〔62〕 L. Nanni, A. Lumini, A. Prototype reduction techniques: a comparison among different approaches, Expert Systems with Applications, Vol. 38, pp. 11820-11828, 2011.
〔63〕 S. García, J. Derrac, J.R. Cano, F. Herrera, Prototype selection for nearest neighbor classification: taxonomy and empirical study, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34 (3), pp. 417-435, 2012.
〔64〕 D. Pyle, Data preparation for data mining, Morgan Kaufmann, 1999.
〔65〕 X.-B. Li and V.S. Jacob, “Adaptive data reduction for large-scale transaction data,” European Journal of Operational Research, Vol. 188(3), pp. 910-924, 2008.
〔66〕 T. Reinartz, “A unifying view on instance selection,” Data Mining and Knowledge Discovery, Vol. 6, pp. 191-210, 2002.
〔67〕 J.F. McCloskey, “The Beginnings of Operations Research: 1934-1941,” Operations Research, Vol. 35(1), pp. 143-152, Jan. - Feb. 1987.
〔68〕 P. Borm, H. Hamers, R. Hendrickx “Operations research games: A survey,” TOP, Vol. 9(2), pp. 139-199, 2001.
〔69〕 S. J. Noronha and V. V. S. Sarma, “Knowledge-Based Approaches for Scheduling Problems: A Survey,” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, Vol. 3(2), June 1991.
〔70〕 Z.L. Chen, N.G. Hall, “Supply Chain Scheduling: Conﬂict and Cooperation in Assembly Systems,” Operations Research, Vol. 55(6), pp. 1072–1089, November–December 2007.
〔71〕 R. Kolisch, Make-to-Order Assembly Management, Operations Scheduling, pp. 93-115, 2001.
〔72〕 M.R. Garey, D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, Freeman, San Francisco, 1979.
〔73〕 B.A. Norman, “Scheduling flowshops with finite buffers and sequence-dependent setup times,” Computers and Industrial Engineering, Vol. 36(1), pp. 163–177, 1999.
〔74〕 T. Sawik, “Mixed integer programming for scheduling flexible lines with limited intermediate buffers,” Mathematical and Computer Modeling, Vol. 31, pp. 39–52, 2000.
〔75〕 M. Nawaz, E. Enscore, I. Ham, “A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem,” Omega, Vol. 11(1), 91–95, 1983.
〔76〕 F.A. Ogbu, D.K. Smith, “The application of the simulated annealing algorithm to the solution of the n/m/Cmax flowshop problem,” Computers and Operations Research, Vol. 17(3), pp. 243–253, 1990.
〔77〕 C.R. Reeves, “A genetic algorithm for flowshop sequencing,” Computers and Operations Research, Vol. 22(1), pp. 5–13, 1995.
〔78〕 P. Ball, Natural strategies for the molecular engineer, Nanotechnology, Vol. 13, pp. 15-28, 2002.
〔79〕 H.T. Odum, Ecological and general systems: an introduction to systems ecology, University Press of Colorado, 1994.
〔80〕 M. Pollan, The year in ideas: A-Z., Genetic pollution, The New York Times, December 9, 2001.
〔81〕 N.C. Ellstrand, Dangerous liaisons: when cultivated plants mate with their wild relatives, Johns Hopkins University Press, 2003.
〔82〕 S. Mitaim, B. Kosko, “What is the best shape for a fuzzy set in function approximation?” IEEE International Conference on Fuzzy Systems, pp. 1237-1243, 1996.
〔83〕 D. Guo, R. Guo, C. Thiart, “Predicting air pollution using fuzzy membership grade Kriging,” Computers Environment and Urban Systems, Vol. 31, pp. 33–51, 2007.
〔84〕 T.H. Emigh, “Comparison of tests for Hardy–Weinberg equilibrium,” Biometrics, Vol. 36 (4), pp. 627-642, 1980.
〔85〕 C. Stern, “Wilhelm Weinberg,” Genetics, Vol. 47, pp. 1–5, 1962.
〔86〕 J.J. Flynn, A.R. Wyss, “Recent advances in South American mammalian paleontology,” Trends in Ecology and Evolution, Vol.13(11), pp. 449-454, 1998.
〔87〕 K.P. Koepfli, M.E. Gompper, E. Eizirik, C.C. Ho, L. Linden, J.E. Maldonado, R.K. Wayne, Phylogeny of the Procyonidae (Mammalia: Carvnivora): Molecules, morphology and the Great American Interchange, Molecular Phylogenetics and Evolution, Vol. 43 (3), pp. 1076-1095, 2007.
〔88〕 G.S. Morgan, Late Rancholabrean mammals from southernmost Florida and Neotropical influence in Florida Pleistocene Faunas, Smithsonian Contributions to Paleobiology, Vol. 93, pp.15-38, 2002.
〔89〕 I.H. Witten, E. Frank, M. Hall, Data mining: practical machine learning tools and techniques, Morgan Kaufmann, San Francisco, CA, 2011.
〔90〕 J. S. Chun, H. K. Jung and S. Y. Hahn, “A study on comparison of optimization performances between immune algorithm and other heuristic algorithms,” IEEE Transactions on Magnetics. Vol. 34(5), pp. 2972 – 2975, 1998.
〔91〕 D. Davidov, E. Gabrilovich and S. Markovitch, “Parameterized generation of labeled datasets for text categorization based on a hierarchical directory,” ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 250-257, 2004.
〔92〕 G. Forman, “An extensive empirical study of feature selection metrics for text classification,” Journal of Machine Learning Research, vol. 3, pp. 1289-1305, 2003.
〔93〕 E. Gabrilovich and S. Markovitch, “Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5,” International Conference on Machine Learning, pp. 321-328, 2004.
〔94〕 S. Lazebnik, C. Schmid and J. Ponce. “Beyond bags of features: spatial pyramid matching for recognizing natural scene categorization,” IEEE International Conference on Computer Vision and Pattern Recognition, pp. 2169-2178, 2006.
〔95〕 D. W. Aha, D. Kibler and M. K. Albert, “Instance-based learning algorithms,” Machine Learning, vol. 6, no. 1, pp. 37-66, 1991.
〔96〕 D. R. Wilson and T. R. Martinez, “Reduction techniques for instance-based learning algorithms,” Machine Learning, vol. 38, pp. 257-286, 2000.
〔97〕 H. Brighton and C. Mellish, “Advances in instance selection for instance-based learning algorithms,” Data Mining and Knowledge Discovery, vol. 6, pp. 153-172, 2002.

指導教授

蔡志豐(Chih-Fong Tsai)

審核日期

2015-7-21

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