J. J. Buckley, Y. Hayashi, Fuzzy genetic algorithm and applications, Fuzzy Sets and Systems 61 (1994) 129--136. Home/Search Document Not in Database Summary Related Articles Check |
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On The Formulation Of Optimization Under Elastic.. - Bouchon-Meunier.. (1994) (Correct)
....set X C, and the resulting more realistic optimization problem will take the following form: Given: ffl a fuzzy set C; ffl a function f that maps fuzzy sets into real numbers. To find: a fuzzy set X for which f(X) max X:X C : Such a problem have been formulated in (Buckley 1994) [10]. Even for simple functions f , this problem is too complicated to be solved by known analytical techniques. Therefore, in (Buckley 1994) 10] a genetic algorithm is used (successfully) to solve it. Acknowledgments. This work was partially supported by NSF grants No. CDA 9015006 and No. ....
....f that maps fuzzy sets into real numbers. To find: a fuzzy set X for which f(X) max X:X C : Such a problem have been formulated in (Buckley 1994) 10] Even for simple functions f , this problem is too complicated to be solved by known analytical techniques. Therefore, in (Buckley 1994) [10], a genetic algorithm is used (successfully) to solve it. Acknowledgments. This work was partially supported by NSF grants No. CDA 9015006 and No. EEC 9322370, and by NASA Grant Np. NAG 9 757. We are thankful to Lotfi Zadeh for his valuable remarks and discussions, to James Buckley, Didier ....
J. J. Buckley, Y. Hayashi, Fuzzy genetic algorithm and applications, Fuzzy Sets and Systems 61 (1994) 129--136.
An Indexed Bibliography of Genetic Algorithms with Fuzzy Systems - Alander (1999) (11 citations) (Correct)
....Applications of Arti cial Intelligence, 286, 349] Engneering Applications of Arti cial Intelligence, 272] Eur. Trans. Electr. Power (Germany) 283] European Journal of Operational Research, 300, 308, 309, 310] Foundations of Computing and Decision Sciences, 63] Fuzzy Sets and Systems, [47, 488, 60, 515, 119, 137, 157, 715, 717] Fuzzy Sets and Systems (Netherlands) 548, 570, 226, 235] Fuzzy Sets Syst. Netherlands) 196, 265, 721, 722, 724] Fuzzy Sets. Syst. Netherlands) 214, 723] Fuzzy Systems Arti cial Intelligence Reports and Letters, 50] High Technol. Lett. China) 294] IEE Proceedings, Control ....
....Xu, 600] Bowerman, C. G. D. 628] Boyd, R. 323] Bradford, C. 628] Brandstatter, B. 252] Brannon, Evelyn, 743] Braunsforth, S. 182] Braunstingl, R. 69, 735] Brown, M. 586] Bruijn, P. 414] Buckles, Bill P. 34, 319, 320] Buckley, J. J. 573, 636] Buckley, James J. [459, 47, 324] Buczak, A. L. 70] Buczak, Anna L. 562] Buhusi, C. 12] Buiu, C. 112] Bull, Lawrence, 159] Bunke, H. 765] Burgard, W. 11] Burkhardt, Diana, 499, 526] Bush, B. 559] Cabestaing, Fran cois, 655] Cadenas, Jose Manuel, 374, 492, 121] Campbell, John A. 19] Caponetto, R. ....
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James J. Buckley and Yoichi Hayashi. Fuzzy genetic algorithm and applications. Fuzzy Sets and Systems, 61(2):129-136, 24. January 1994. * CCA 24790/94 ga94bBuckley.
Fuzzy multiple criteria decision making: Recent developments - Fullér, Carlsson (1996) (Correct)
.... the conflicting objectives (this was carried out in an intuitive fashion in the real case) or (ii) alternate optima for combinations of subsets of the objectives during a negotiated interval (this was also attempted by representatives of the Seller, but without any success) Buckley and Hayashi [12] introduced fuzzy genetic algorithms to (approximately) solve fuzzy optimization problems. Fuzzy genetic algorithms look like an interesting method of producing approximate solutions to fuzzy optimization problems when the variables can be arbitrary discrete fuzzy subsets of certain intervals. 3 ....
J.J.Buckley and Y.Hayashi, Fuzzy genetic algorithm and applications, Fuzzy Sets and Systems, 61(1994) 129-136.
Fuzzy Sets and Operations Research. Perspectives - Herrera, Verdegay (1995) (Correct)
.... of neural networks for solving the fuzzy multiobjective 0 1 programming [52] and the maximum cut in a non directed graph with fuzzy weights [4] simulated annealing for solving fuzzy flowshop scheduling [37] and genetic algorithms for solving maximum flow in a network with fuzzy capacities [5, 27], fuzzy flowshop scheduling problems [38] vehicle routing problem with fuzzy due time [9] etc. Let us consider, for instance, the interface between Genetic Algorithms (GAs) and Fuzzy Sets Theory. GAs are search algorithms that use operations found in natural genetics to guide the trek through a ....
....that allow GA performance to be analyzed from a human point of view. b) To manage problems in an imprecise environment, where the inherent imprecision is modeled by means of Fuzzy Sets, solving fuzzy optimization problems. Two ways to manage fuzzy information with GAs have been presented, [5, 27], both with a similar root, but considering a different way to obtain a real value for defining the selection process. The differences lie in the conception of the variables, the first proposal considers variables with fuzzy values representing these in a chromosome [5] and the second one ....
[Article contains additional citation context not shown here]
J.J. Buckley, Y. Hayashi, Fuzzy Genetic Algorithms and Applications. Fuzzy Sets and Systems 61 (1994) 129-136.
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