Chen Disser (1121212), страница 29
Текст из файла (страница 29)
Rough large deviation estimates for the optimal convergence speed exponentof generalized simulated annealing algorithms. Technical report, LMENS-94-8, EcoleNormale Superieure, France, 1994.[92] T. Wang. Global Optimization for Constrained Nonlinear Programming. Ph.D. Thesis,Dept. of Computer Science, Univ. of Illinois, Urbana, IL, December 2000.[93] T. Westerlund and F. Pettersson. A cutting plane method for solving convex minlp[83] B. Wah and Y. X. Chen. Constraint partitioning in penalty formulations for solvingtemporal planning problems. Artificial Intelligence, (accepted for publication) 2005.[84] B.
W. Wah and Y. X. Chen. Constrained genetic algorithms and their applicationsin nonlinear constrained optimization. In Proc. Int’l Conf. on Tools with ArtificialIntelligence, pages 286–293. IEEE, November 2000.[85] B. W. Wah and Y. X. Chen. Optimal anytime constrained simulated annealing for constrained global optimization. Sixth Int’l Conf. on Principles and Practice of ConstraintProgramming, pages 425–439, September 2000.[86] B. W. Wah and Y. X. Chen. Hybrid constrained simulated annealing and genetic algorithms for nonlinear constrained optimization. In Proc.
IEEE Congress on EvolutionaryComputation, pages 925–932, May 2001.[87] B. W. Wah and Y. X. Chen. Hybrid evolutionary and annealing algorithms for nonlinear discrete constrained optimization. Int’l Journal of Computational Intelligence andApplications, 3(4):331–355, December 2003.[88] B. W. Wah and T. Wang. Constrained simulated annealing with applications in nonlinear constrained global optimization. In Proc. Int’l Conf. on Tools with ArtificialIntelligence, pages 381–388.
IEEE, November 1999.problems. Computers and Chemical Engineering, 19:S131–S136, 1995.[94] D. Wilkins. Can AI planners solve practical problems?[95] J. Willis, G. Rabideau, and C. Wilklow. The Citizen Explorer scheduling system. InProc. Aerospace Conf. IEEE, 1999.[96] S. Wolfman and D. Weld. Combining linear programming and satisfiability solving forresource planning. The Knowledge Engineering Review, 15(1), 2000.[97] Z. Wu.
Discrete Lagrangian Methods for Solving Nonlinear Discrete Constrained Optimization Problems. M.Sc. Thesis, Dept. of Computer Science, Univ. of Illinois, Urbana,IL, May 1998.[98] Z. Wu. The Theory and Applications of Nonlinear Constrained Optimization using Lagrange Multipliers.
Ph.D. Thesis, Dept. of Computer Science, Univ. of Illinois, Urbana,IL, May 2001.[99] H. H. Zhang. Improving Nonlinear Constrained Search Algorithms Through ConstraintRelaxation. M.Sc. Thesis, Dept. of Computer Science, Univ. of Illinois, Urbana, IL,August 2001.[89] B. W. Wah and T. Wang. Simulated annealing with asymptotic convergence for nonlinear constrained global optimization. In Proc. Principles and Practice of ConstraintProgramming, pages 461–475. Springer-Verlag, October 1999.[90] B.
W. Wah and T. Wang. Tuning strategies in constrained simulated annealing fornonlinear global optimization. Int’l J. of Artificial Intelligence Tools, 9(1):3–25, March2000.[91] B. W. Wah and Z. Wu. The theory of discrete Lagrange multipliers for nonlinear discreteoptimization. In Proc. Principles and Practice of Constraint Programming, pages 28–42.Springer-Verlag, October 1999.165Computational Intelligence,pages 232–246, 1990.166VitaYixin Chen received his B.S. degree from the Department of Computer Science, Universityof Science and Technology of China in 1999, and his M.S. degree in Computer Science fromthe University of Illinois at Urbana-Champaign in May, 2001.His research interests include nonlinear optimization, artificial intelligence, data warehousing, data mining, operations research, and algorithm complexity.167.
![](https://s.studizba.com/z.php?f=/templates/si/i/noavatar.png&w=100&h=100&t=1"){kind=avatar}
