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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 于天立(Tian-Li Yu) | |
| dc.contributor.author | Ta-Chun Lien | en |
| dc.contributor.author | 連大鈞 | zh_TW |
| dc.date.accessioned | 2021-06-15T04:47:32Z | - |
| dc.date.available | 2010-08-20 | |
| dc.date.copyright | 2010-08-20 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-08-04 | |
| dc.identifier.citation | Beasley, D., Bull, D. R., & Martin, R. R. (1993). A sequential niche technique for multimodal function optimization. Evolutionary Computation, 1 , 101–125.
Becker, G. S. (1976). Altruism, egoism, and genetic fitness: Economics and sociobiology. Journal of Economic Literature, 14 (3), 817–26. Bennett, P. G. (1995, April). Modelling decisions in international relations: Game theory and beyond. Mershon International Studies Review, 39 (1), 19–52. Brandenburger, A., & Nalebuff, B. (1996). Co-opetition: A revolution mindset that combines competition and cooperation: The game theory strategy that’s changing the game of business. New York: Doableday Press. B‥ack, T. (1993). Optimal mutation rates in genetic search. In Proceedings of the fifth International Conference on Genetic Algorithms (pp. 2–8). Morgan Kaufmann. Cellini, R., & Lambertini, L. (2004). Dynamic oligopoly with sticky prices: Closedloop, feedback, and open-loop solutions. Journal of Dynamical and Control Systems, 10 , 303–314. Chakraborty U.K., J. C. (2003). An analysis of gray versus binary encoding in genetic search. In Information Sciences, Volume 156 (pp. 253–269). Chen, J.-H., Chao, K.-M., Godwin, N., Reeves, C., & Smith, P. (2002). An automated negotiation mechanism based on co-evolution and game theory. In SAC’02: Proceedings of the 2002 ACM symposium on Applied computing (pp. 63–67). ACM. Chiaramonte, M., & Chiaramonte, L. (2008). An agent-based nurse rostering system under minimal staffing conditions. International Journal of Production Economics, 114 (2), 697 – 713. Special Section on Logistics Management in Fashion Retail Supply Chains. Corno, F., Prinetto, P., Rebaudengo, M., Reorda, M. S., & Squillero, G. (1997). A genetic algorithm for the computation of initialization sequences for synchronous sequential circuits. Asian Test Symposium, 56. Cournot, A. A. (1838). Recherches sur les principles mathematiques de la th′eorie des richesses. Libraire des sciences politiques et sociales. Dawkins, R. (1976). The selfish gene. Oxford: Oxford University Press. De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems. Doctoral dissertation, University of Michigan, Ann Arbor, MI, USA. De Jong, K. A. (2006). Evolutionary computation. MIT Press. De Jong, K. A., & Spears, W. M. (1992). A formal analysis of the role of multipoint crossover in genetic algorithms. Annals of Mathematics and Artificial Intelligence, 5 , 1–26. De Jong, K. E., Stanley, K. O., & Wiegand, R. P. (2007). Introductory tutorial on coevolution. Dhira, K. D. (2001). Real-coded evolutionary algorithms with parent-centric recombination. Dutta, P. (1999). Strategies and games: theory and practice. MIT Press. Edgeworth, F. Y. (1881). Mathematical psychics. Kegan Paul. Estes, R. (1967). The comparative behavior of grant’s and thomson’s gazelles. Journal of Mammalogy, 48 (2), 189–209. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA. Goldberg, D. E., & Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. In Rawlins, G. (Ed.), Foundations of Genetic Algorithms (pp. 69–93). Grano, D., Medeiros, D., & Eitel, D. (2009). Accommodating individual preferences in nurse scheduling via auctions and optimization. Health Care Management Science, 12 , 228–242. Guth, W. (1988). Game theory and the nuclear arms race - the strategic position of western europe -. European Journal of Political Economy, 4 (2), 245–261. Harik, G. (1994). Finding multiple solutions in problems of bounded difficulty (Technical Report). Harik, G. (1995). Finding multimodal solutions using restricted tournament selection. In Eshelman, L. (Ed.), Proceedings of the Sixth International Conference on Genetic Algorithms (pp. 24–31). San Francisco, CA: Morgan Kaufmann. Harsanyi, J. C. (1977). Rational behavior and bargaining equilibrium in games and social situations. Cambridge University Press. Isaacs, R. (1999). Differential games: A mathematical theory with applications to warfare and pursuit, control and optimization. Dover Publications. Kannai, Y. (1992, June). The core and balancedness. In Handbook of Game Theory with Economic Applications, Handbook of Game Theory with Economic Applications (Chapter 12, pp. 355–395). Elsevier. L. Nilsson, Lars Johnsson, L. R., & Randrianjohany, E. (1987). Angraecoid orchids and hawkmoths in central madagascar: Specialized pollination systems and generalist foragers. Biotropica, 19 , 310–318. Lin, W.-K. (2009). The co-evolvability of games in coevolutionary genetic algorithms. Master’s thesis, National Taiwan University. Lin, W.-K., & Yu, T.-L. (2009). Co-evolvability of games in coevolutionary genetic algorithms. In GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation (pp. 1869–1870). New York, NY, USA: ACM. Luce, R. D., & Raiffa, H. (1957). Games and decisions: introduction and critical survey. New York: John Wiley & Sons. Michalewicz, Z. (1996). Genetic algorithms + data structures = evolution programs (3rd ed.). London, UK: Springer-Verlag. Miller, B. L., & Goldberg, D. E. (1995). Genetic algorithms, tournament selection, and the effects of noise. Complex Systems, 9 , 193–212. Nash, J. (1950). Equilibrium points in n-person games. Oliehoek, F., de Jong, E., & Vlassis, N. (2006). The parallel nash memory for asymmetric games. In GECCO ’06: Proceedings of the 8th annual conference on Genetic and evolutionary computation (pp. 337–344). New York, NY, USA: ACM. Ono, I., Kita, H., & Kobayashi, S. (2003). A real-coded genetic algorithm using the unimodal normal distribution crossover. Advances in evolutionary computing: theory and applications, 213–237. Osogami, T., & Imai, H. (2000). Classification of various neighborhood operations for the nurse scheduling problem. In ISAAC ’00: Proceedings of the 11th International Conference on Algorithms and Computation (pp. 72–83). London, UK: Springer-Verlag. Phelps, S., Marcinkiewicz, M., & Parsons, S. (2006). A novel method for automatic strategy acquisition in n-player non-zero-sum games. In AAMAS ’06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (pp. 705–712). ACM. Poon, P. W., & Carter, J. N. (1995). Genetic algorithm crossover operators for ordering applications. Computers and Operations Research, 22 , 135–147. Popovici, E., & De Jong, K. (2006). The dynamics of the best individuals in co-evolution. Natural Computing: an international journal, 5 , 229–255. Reeves, D., Wellman, M., MacKie-Mason, J., & Osepayshvili, A. (2005). Exploring bidding strategies for market-based scheduling. Decision Support Systems, 39 (1), 67–85. Russell, Stuart J.; Norvig, P. (2003). Artificial intelligence: A modern approach. Prentice Hall. Shapley, L. (1953a). Stochastic games, proceedings of national academy of science. Volume 39 (pp. 1095–1100). Shapley, L. (1953b). A value for n-person games, in: Contributions to the theory of games. Princeton, NJ: Princeton University Press. Starkweather, T., Mcdaniel, S., Whitley, D., Mathias, K., & Whitley, D. (1991). A comparison of genetic sequencing operators. von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton University Press. Watson, J. (2007). Strategy: An introduction to game theory. W. W. Norton & Company. Weibull, J. W. (1997). Evolutionary game theory. Cambridge, MA: The MIT Press. Yao, X., & Darwen, P. (1994). An experimental study of n-person iterated prisoner's dilemma games. Informatica, 18 , 435–450. Yip, K. Y., Patel, P., Kim, P. M., Engelman, D. M., McDermott, D., & Gerstein, M. (2008). An integrated system for studying residue coevolution in proteins. Bioinformatics, 24 , 290–292. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/45860 | - |
| dc.description.abstract | 本論文乃研究共同演化的基因演算法是否能在完全自利的考量下，演化出合作策略。本論文的研究對象為以拍賣方式解決人力分配的問題，因為該問題同時存在著競爭和合作。為了減輕分析該問題的負擔，本研究先將問題抽象化為一個納許遊戲：資源投標遊戲。本論文建立資源投標遊戲的數學模型並進行分析，且另外提供了幾個有研究價值的特例。其中一個特例：c-mNE，因為存在著合作模式，所以被進一步的研究。本研究在該特例上進行了許多實驗，包含了各種不同的演化機制及多樣的自利考量評分函數。根據實驗結果，我們認為若有正確的演化機制可以保留合作策略並剔除競爭策略，則共同演化的基因演算法是可以演化出合作策略。 | zh_TW |
| dc.description.abstract | This thesis examines whether coevolutionary genetic algorithms can evolve cooperative strategies under pure egoistic considerations. Since both competition and
cooperation coexist in an auction-based manpower allocation problem, the problem is adopted for further investigation. To alleviate analytical burden, the problem is abstracted to a resource-bidding game under the Nash game framework. A mathematical model for the resource-bidding game is defined and several special cases are illustrated. One of these special cases, c-mNE, is further investigated due to the existance of cooperative modes. Various kinds of egoistic fitness functions and evolutionary mechanisms are experimented on c-mNE. Based on the experimental results, this thesis concludes that coevolutionary mechanisms which properly eliminate aggressive strategies and preserve cooperative strategies can evolve cooperative modes under the pure egoistic assumption. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T04:47:32Z (GMT). No. of bitstreams: 1 ntu-99-R97921055-1.pdf: 1765556 bytes, checksum: 71173896a3476c354c639adebf4dec52 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | Abstract i
Contents ii List of Figures iv List of Tables vi 1 Introduction 1 2 Background of Game Theory 4 2.1 Definition and Representation Schemes . . . . . . . . . . . . . . . . . 4 2.2 Nash Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3 Background of Genetic Algorithm and Coevolutionary Algorithms 14 3.1 Background of Simple Genetic Algorithm . . . . . . . . . . . . . . . . 14 3.2 Niching Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3 Restricted Tournament Selection . . . . . . . . . . . . . . . . . . . . 20 3.4 Coevolutionary Genetic Algorithms . . . . . . . . . . . . . . . . . . . 21 4 Mathematical Model 23 4.1 Model Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.2 Special Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.3 Analysis of c-mNE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 5 Methods and Experimental Results 29 5.1 SGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 5.2 SGA + RTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.3 SGA + RTS + SFF . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 5.4 SGA + mRTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 6 Discussions 42 6.1 Incapability of Mixed Strategies . . . . . . . . . . . . . . . . . . . . . 42 6.2 Loss of Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 6.3 Lack of Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 7 Conclusions 51 Bibliography 54 | |
| dc.language.iso | en | |
| dc.subject | 共同演化 | zh_TW |
| dc.subject | 拍賣 | zh_TW |
| dc.subject | 基因演算法 | zh_TW |
| dc.subject | 遊戲理論 | zh_TW |
| dc.subject | Game Theory | en |
| dc.subject | Genetic Algorithms | en |
| dc.subject | Auction | en |
| dc.subject | Coevolution | en |
| dc.title | 利己主義下合作策略的共同演化 | zh_TW |
| dc.title | Coevolution of Cooperative Strategies under Egoism | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張時中,陳建宏 | |
| dc.subject.keyword | 基因演算法,共同演化,遊戲理論,拍賣, | zh_TW |
| dc.subject.keyword | Genetic Algorithms,Coevolution,Game Theory,Auction, | en |
| dc.relation.page | 58 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-08-04 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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