فهرست:
فصل اول
1-مقدمه.................................................................................................................................................... 2
1-1- مقدمه.............................................................................................................................................. 2
1-2- انگیزه............................................................................................................................................ 3
1-3- شرح مسئله................................................................................................................................... 4
1-4- چالشها........................................................................................................................................ 5
1-5- اهداف پایان نامه.......................................................................................................................... 7
فصل دوم.
2- پیشینه تحقیق................................................................................................................................... 9
2-1- مقدمه......................................................................................................................................... 10
2-2- حوزه تکامل قوانین فازی........................................................................................................... 11
2-3-یادگیری سیستمهای طبقه بندی کننده فازی........................................................................... 12
2-3-1- یادگیری سیستمهای طبقه بندی کننده فازی بر اساس الگوریتم ژنتیک....................... 12
2-3-2- الگوریتمهای تکامل همزمان............................................................................................. 22
2-3-3- یادگیری سیستمهای طبقه بندی کننده فازی با استفاده از الگوریتم ازدحام ذرات ........ 24
2-3-4- یادگیری سیستمهای طبقه بندی کننده فازی با استفاده از الگوریتم زنبور عسل........... 25
2-3-5- یادگیری سیستمهای طبقه بندی کننده فازی با استفاده از الگوریتم مورچگان ......... 26
2-4- الگوریتم رقابت استعماری.......................................................................................................... 26
2-4-1- ویژگیهای الگوریتم رقابت استعماری....................................................................................... 28
2-4-2-کاربردهای الگوریتم رقابت استعماری........................................................................................ 28
2-5-جمع بندی ............................................................................................................................ 30
فصل سوم
3- روش تحقیق .................................................................................................................................... 32
3-1- مقدمه ....................................................................................................................................... 33
3-2- سیستمهای فازی....................................................................................................................... 34
3-2-1- سیستمهای استنتاج فازی................................................................................................... 34
سیستمهای فازی Mamdani............................................................................ 34
سیستمهای فازی Sugeno......................................................................................................... 35
سیستمهای فازی Tsukamato.................................................................................................. 35
3-2-2- طبقه بندی کنندههای فازی.............................................................................................. 36
تابع استدلال فازی................................................................................................................. 36
معیار ارزیابی قوانین ................................................................................................................ 38
3-3- الگوریتم CORE .................................................................................................................... 39
3-4- الگوریتم جزیره ای Ishibuchi برای استخراج قوانین ............................................................ 39
3-5- الگوریتم GBML-IVFS-amp ........................................................................................... 41
3-6- الگوریتم GNP برای وزندهی به قوانین فازی ............................................................................. 42
3-7- الگوریتم TARGET .............................................................................................................. 42
3-8- الگوریتم SGERD ................................................................................................................. 43
3-9- الگوریتم رقابت استعماری .............................................................................................................. 44
3-9-1- مقدرادهی اولیه امپراطوریها................................................................................................... 45
3-9-2- عملگر Assimilation..................................................................................................... 46
3-9-3- استراتژیهای بهینه سازی میتنی بر تکامل اجتماعی-سیاسی........................................... 47
3-10- الگوریتمهای پیشنهادی ......................................................................................................... 48
3-10-1- هدف استفاده از ICA برای الگوریتم پیشنهادی ........................................................... 48
3-10-2- وزندهی به قوانین فازی.................................................................................................. 48
3-10-3- الگوریتم پیشنهادی برای تکامل قوانین فازی................................................................ 52
قوانین خاص و عام................................................................................................................ 52
روش پیشنهادی برای تولید قوانین فازی ............................................................................ 53
تابع برازش پیشنهادی......................................................................................................... 54
3-11-جمع بندی .......................................................................................................................... 57
فصل چهارم
نتایج آزمایشات.................................................................................................................................... 58
4-1- معیارهای ارزیابی....................................................................................................................... 59
4-2-مجموعه دادهها .......................................................................................................................... 60
4-2-1-مجموعه داده KEEL........................................................................................................ 60
4-2-2-مجموعه داده UCI.................................................................................................................. 61
4-3- الگوریتم پیشنهادی برای وزندهی به قوانین.............................................................................. 61
4-3-1-پارامترها و تنظیمات سیستم در پیاده سازی....................................................................... 61
4-3-2-مقایسه الگوریتم پیشنهادی با طبقه بندی کنندههای فازی............................................... 62
4-3-3-مقایسه الگوریتم پیشنهادی با طبقه بندی کنندههای غیر فازی........................................ 66
4-4- الگوریتم پیشنهادی برای تولید قوانین فازی بهینه........................................................................... 68
4-4-1-پارامترها و تنظیمات سیستم در پیاده سازی یادگیری ساختار قوانین فازی............................ 68
4-4-2-انتخاب ویژگی........................................................................................................................... 69
4-4-3-ارزیابی الگوریتم یادگیری ساختار قوانین با روشهای فازی..................................................... 70
4-4-4-ارزیابی الگوریتم با روشهای غیر فازی..................................................................................... 72
4-5- جمع بندی .............................................................................................................................. 73
فصل پنجم
جمع بندی و پیشنهادات...................................................................................................................... 76
اختصارات......................................................................... ....................................................................................... 78
واژهنامه فارسی به انگلیسی.................................................................................................................................................................................................................................................. 79
واژه نامه انگلیسی به فارسی........................................................................................ 80
فهرست منابع..................................................................................... ..................................................................82
منبع:
Zadeh. "Fuzzy sets". Information and Control, (8):338–352, 1965.
H. Ishibuchi and T. Yamamoto, "Fuzzy Rule Selection by Multi-Objective Genetic Local Search Algorithms and Rule Evaluation Measures in Data Mining," Journal of Fuzzy Sets and Systems, vol. 141, no. 1, pp. 59-88, January 2004.
H. Ishibuchi, T. Nakashima and T. Murata, "Three-objective genetics-based machine learning for linguistic rule extraction," Journal of Information Sciences, vol. 136, no. 4, pp. 109-133, 2001.
Eghbal G. Mansoori, Mansoor J. Zolghadri and Seraj D. Katebi, "SGERD: A Steady-State Genetic Algorithm for Extracting Fuzzy Classification Rules From Data", Journal of IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 16, no. 4, pp. 1061-1071, 2008.
H. Ishibuchi, K. Nozaki and H. Tanaka, "Distributed representation of fuzzy rules and its application to pattern classification," Journal of Fuzzy Sets and Systems, vol. 52, no. 1, pp. 21-32, Nov 1992.
Marghny H. Mohamed, "Rules extraction from constructively trained neural networks based on genetic algorithms," Journal of Neurocomputing, vol. 74, no. 17, pp. 3180-3192, October 2011.
J. Gomez, D. Dasgupta, O. Nasraoui, and F. Gonzalez. Complete expression trees for evolving fuzzy classifier systems with genetic algorithms. In Proceedings of the North American Fuzzy Information Processing Society Conference NAFIPS-FLINTS 2002, pages 469–474, 2002.
A. A. Freitas. A survey of evolutionary algorithms for data mining and knowledge discovering. In Advances in Evolutionary Computation. A. Ghosh and S. Tsutsui. (Eds.). Springer-Verlag, 2001.
Q.Zhang, C.Wang, "Using Genetic Algorithm to Optimize Artificial Neural Network: A Case Study on Earthquake Prediction", Second International conference on Genetic and Evolutionary Computing, pp. 128-131, Sep 2008.
Jesús Alcalá-Fdez, Rafael Alcalá, María José Gacto and Francisco Herrera, "Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms," Fuzzy Sets and Systems, vol. 160, no. 7, pp. 905-921, April 2009.
E. Atashpaz-Gargari and C. Lucas," Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition," IEEE Congress on Evolutionary computation, pp. 4661-4667, September 2007.
J. Casillas, F. Herrera, R. Pérez, M.J. del Jesus, P. Villar, Special issue on genetic fuzzy systems and the interpretability—accuracy tradeoff—editorial, International Journal of Approximate Reasoning 44 (1) (2007) 1–3.
F. Herrera, Genetic fuzzy systems: taxonomy, current research trends and prospects, Evolutionary Intelligence 1 (2008) 27–46.
J. Casillas, B. Carse, Preface: genetic fuzzy systems: recent developments and future directions, Special issue on genetic fuzzy systems: recent developments and future directions, Soft Computing 13 (2009) 417–418.
T. Murata, S. Kawakami, H. Nozawa, M. Gen, and H. Ishibushi. Three-objective genetic algorithmsfor designing compact fuzzy rule-based systems for pattern classification problems. In Proceedings of the Genetic and Evolutionary Computation Conference GECCO’01, pages 485–492, 2001.
C. Karr. Genetic algorithms for fuzzy controllers. AI Experts, pages 26–33, 1991.
M. Kudo, P. Somol, P. Pudil, M. Shimbo and J Sklansky, " Comparison of Classifier-Specific Feature Selection Algorithms",pattern recognition, vol. 1876, pp.678-686, 2000.
Alex A. Freitas ,"Data Mining and Knowledge Discovery with Evolutionary Algorithms", Springer; 2002 edition Aug 2002.
Valenzuela-Rendon ," The fuzzy classifier system: A classifier system for continuously varying variables", In: Proc. of 4th International Conference on Genetic Algorithms (ICGA'91), pp 346-353, 1991.
A. Parodi, P. Bonelli, "A New Approach to Fuzzy Classifier Systems",. ICGA 1993: pp.223-230, 1993.
H. Ishibuchi, K. Nozaki and H. Tanaka, "Distributed representation of fuzzy rules and its application to pattern classification," Journal of Fuzzy Sets and Systems, vol. 52, no. 1, pp. 21-32, Nov 1992.
H. Ishibuchi , T. Yamamoto , T. Nakashima, " Hybridization of fuzzy GBML approaches for pattern classification problems ", IEEE Transaction on Systems, Man and Cybernetics, Volume 35, Issue 2, pp. 359-365, April 2005.
A. Bonarini. Evolutionary learning of fuzzy rules: competition and coop- eration. In W. Pedrycz, editor, Fuzzy Modelling: Paradigms and Practice, pp. 265–284. Norwell, MA: Kluwer Academic Press, 1996. 4.2.
A.Bonarini and V.Trianni, "Learnign classifier systems for multi-agent coordination Information Sciences", 136:215-239, 2001.3.4.
J.Casillas, B. Carse, L. Bull, " Fuzzy-XCS: A Michigan Genetic Fuzzy System", IEEE Transaction on Fuzzy Systems, Vol. 15, Issue 4, pp. 536-550, Aug 2007.
T. Murata H. Ishibuchi and I.B.Turksen. Selecting linguistic classification rules by two-objective genetic algorithms. In Proceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics, pages 1410– 1415, Vancouver, Canada, October 1995. IEEE. 3.4.3.
M. Russo, "GEFREX: A GEnetic Fuzzy Rule EXtractor", International Journal of Knowledge-Based and Intelligent Engineering Ssytems, pp.49-59, 1999.
J.A. Sanz, A. Fernández, H Bustince and F.Herrera, " Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning ", journal of Information Sciences 180 (2010) 3674–3685.
J.S. Aguilar-Ruiz, J.C. Riquelme, and M. Toro. (2003). Evolutionary learning of hierarchical decision rules. IEEE Transactions on Systems, Man, and Cyberneticts - Part B: Cybernetics, 33(2):324–331.
J. Brian Gray and Guangzhe Fan. (2008). Classification tree analysis using TARGET. Computational Statistics & Data Analysis, 52(3):1362–1372.
A. Gonz´alez, R. P´erez, " SLAVE: A Genetic Learning System Based on an Iterative Approach ", IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 7, NO. 2, pp. 176-191, APRIL 1999.
F. Farahbod, M. Eftekhari, " AN EVOLUTIONARY APPROACH FOR LEARNING RULEWEIGHTS IN FUZZY RULE-BASED CLASSIFICATION SYSTEMS" , International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 2012
C. Andrés Pena-Reyes, Moshe Sipper ,"Fuzzy CoCo: A cooperative-coevolutionary approach to fuzzy modeling", IEEE Transactions on Fuzzy Systems, Vol. 9, Issue 5, pp.727-737, 2001.
Mendes, R., R., F., Voznika, F., de B., Freitas, A., A., Nievola, J. C. "Discovering Fuzzy Classification Rules with Genetic Programming and Co-Evolution, In Principles of Data Mining and Knowledge Discovery (Proc. 5th European Conference PKDD 2001) –Lecture Notes in Artificial Intelligence, Springer-Verlag.
Mendes, R., R., F., Voznika, F., de B., Freitas, A., A., Nievola, J. C. "Discovering Fuzzy Classification Rules with Genetic Programming and Co-Evolution, In Principles of Data Mining and Knowledge Discovery (Proc. 5th European Conference PKDD 2001) –Lecture Notes in Artificial Intelligence, Springer-Verlag.
Mendes, R., R., F., Voznika, F., de B., Freitas, A., A., Nievola, J. C. "Discovering Fuzzy Classification Rules with Genetic Programming and Co-Evolution, In Principles of Data Mining and Knowledge Discovery (Proc. 5th European Conference PKDD 2001) –Lecture Notes in Artificial Intelligence, Springer-Verlag.
H. Ishibuchi, S. Mihara, and Y. Nojima, “Parallel distributed hybrid fuzzy GBML models with rule set migration and training data rotation,” IEEE Trans. on Fuzzy Systems (in press).
Elragal, Hassan M, " Using Swarm Intelligence for Improving Accuracy of Fuzzy Classifiers", International Journal of Electrical & Computer Engineering;2010, Vol. 5 Issue 2, p105, 2010.
F. Beloufa and M.A. Chikh , " Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm ", Computer Methods and Programs in Biomedicine, Volume 112, Issue 1, October 2013, Pages 92–.
R.S. Parpinelli, H.S. Lopes, and A.A. Freitas. (2002). Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation, 6(4):321–332.
M. F. Ganji and M. Saniee Abadeh, "A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis", journal of Expert Systems with Applications 38 (2011) , pp.14650–14659.
R. Rajabioun, F. Hashemzadeh, E. Atashpaz-Gargari, B. Mesgari, F. Rajaei Salmasi, Identification of a MIMO evaporator and its decentralized PID controller tuning using Colonial Competitive Algorithm, In the proceeding of IFAC World Congress, Seoul, Korea, 2008, pp. .9952-9957.
E. Atashpaz-Gargari, F. Hashemzadeh, R. Rajabioun, C. Lucas, Colonial competitive algorithm: A novel approach for PID controller design in MIMO distillation column process, International Journal of Intelligent Computing and Cybernetics. 1(3),pp. 337-355,2008.
H. Sepehri Rad, C. Lucas, Application of Imperialistic Competition Algorithm in recommender systems, In 13th Int'l CSI Computer Conference (CSICC'۰۸), Kish Island, Iran., 2008.
A. Biabangard-Oskouyi, E. Atashpaz-Gargari, N. Soltani, and C. Lucas, “Application of Imperialist Competitive Algorithm for Material Properties Characterization from Sharp Indentation Test,” Int J Eng Simul, vol. 10, no. 1, 2009.
T. Maryam, F. Nafiseh, L. Caro, and T. Fattaneh, “Artificial Neural Network Weights Optimization based on Imperialist Competitive Algorithm.
R. Rajabioun, E. Atashpaz-Gargari, C. Lucas, Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement, Springer Lecture Notes in Computer Science (LNCS). 680-695, 2008.
A. Jasour, E. Gargari, and C. Lucas, “Vehicle Fuzzy Controller Design Using Imperialist Competitive Algorithm,” in Second First Iranian Joint Congress on Fuzzy and Intelligent Systems, Tehran, Iran, 2008.
Lin, J.-L.; Tsai, Y.-H.; Yu, C.-Y.; Li, M.-S. Interaction Enhanced Imperialist Competitive Algorithms, 5, 433-448, 2012.
Shahram Mollaiy Berneti, Mehdi Shahbazian," An Imperialist Competitive Algorithm Artificial Neural Network Method to Predict Oil Flow Rate of the Wells ", International Journal of Computer Applications (0975 – 8887) Volume 26– No.10, July 2011.
H. Ishibuchi, T. Nakashima, T. Morisawa, "Voting in fuzzy rule-based systems for pattern classification problems", Fuzzy Sets and Systems, Volume 103, Issue 2, 16 April 1999, Pages 223–238.
Ishibuchi, H. and T. Yamamoto (2002a). “Fuzzy rule selection by data mining criteria and genetic algorithms,” Proc. of 2002 Genetic and Evolutionary Computation Conference, 399-406.
N. V. Chawla, “C4. 5 and imbalanced data sets: investigating the effect of sampling method, probabilistic estimate, and decision tree structure,” In Proceedings of the ICML (Vol. 3), 2003.
J. Demsar, “Statistical comparisons of classifiers over multiple data sets”, The Journal of Machine Learning Research, vol. 7, pp. 1-30, 2006.