Iterated local search with guided mutation

2006 IEEE Congress on Evolutionary Computation, CEC 2006

Volumn , Issue , 2006, Pages 924-929

  Zhang, Qingfu ^a,b   Sun, Jianyong ^a,b  

^a UNIVERSITY OF ESSEX   (United Kingdom)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

PARALLEL ALGORITHMS; PARAMETER ESTIMATION; PROBLEM SOLVING; STATISTICAL METHODS;

CONVENTIONAL ITERATED LOCAL SEARCH (CILS); GUIDED MUTATION; LOCATION INFORMATION; QUADRATIC ASSIGNMENT PROBLEM;

ITERATIVE METHODS;

EID: 34547270324     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. P. Larrañaga and J. A. Lozano. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, 2002.
2. D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley: Reading, MA, 1989.
3. J. M. Peña, V. Robles, P. Larrañaga, V. Herves, F. Rosales, and M. S. Pérez. Ga-eda: Hybrid evolutionary algorithm, using genetic and estimation of distribution algorithms. In The 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pages 361-371, Ottawa, Canada, 2004.
4. Q. Zhang, J. Sun, and E.P.K. Tsang. Evolutionary algorithm with the guided mutation for the maximum, clique problem. IEEE Transactions on Evolutionary Computation, 9(2):192-200, 2005.
5. Q. Zhang, J. Sun, E.P.K. Tsang, and J.A. Ford. Combination of guided local search and estimation of distribution algorithm for solving quadratic assignment problem. In Proc. of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, pages 42-48, 2004.
6. T. Stützle. Iterated local search for the quadratic assignment problem. Technical Report AIDA-99-03, Darmstadt University of Technology, Computer Science Department, Intellectics Group, 1999.
7. Q. Zhang, J. Sun, E. Tsang, and J. Ford. Estimation of Distribution Algorithm with 2-opt Local Search for the Quadratic Assignment Problem. 2006.
8. R. Battiti and M. Protasi. Handbook of Combinatorial Optimization, volume 1, chapter Approximate Algorithms and Heuristics for MAXSAT, pages 77-148. Kluwer Academic Publishers, 1998.
9. F. Glover and M. Laguna. Tabu Search. Kluwer Academic Publishers, 1998.
10. E. Cela. The Quadratic Assignment Problem: Theory and Algorithms. Kluwer Academic Publishers, 1998.
11. È. D. Taillard. Robust taboo search, for the quadratic assignment problem. Parallel Computing, 17:443-455, 1991.
12. P. Mills and E. P. K. Tsang. Guided local search for solving SAT and weighted MAX-SAT problems. Journal of Automatic Reasoning, Special Issue on Satisfiability Problems, 24:205-223, 2000.
13. V. Nissen. Solving the quadratic assignment problem with clues from nature. IEEE Transactions on Neural Networks, 5(1):66-72, 1994.
14. D. M. Tate and A. E. Smith. A genetic approach to the quadratic assignment problem. Computers and Operations Research, 22(1):73-83, 1995.
15. L. Gambardella, É. Taillard, and M. Dorigo. Ant colonies for the QAP. Journal of the Operations Research Society, 50:167-176, 1999.
16. P. Merz and B. Freisleben. Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Transcations on Evolutionary Computation, 4(4):337-352, 2000.
17. V-D. Cung, T. Mautor, P. Michelon, and A. Tavares. A. scatter search based approach for the quadratic assignment problem. In T. Bäck, Z. Michalewicz, and X. Yao, editors, Proc. of the 1997 IEEE International Conference on Evolutionary Computation (ICEC), pages 165-170. IEEE Press, 1997.
18. E. S. Buffa, G.C. Armour, and T.E. Vollmann. Allocating facilities with CRAFT. Hardware Business Review, pages 136-158, March 1964.
19. J. Sun. Hybrid Estimation of Distribution Algorithms for Optimization Problems. PhD thesis, Department of Computer Science, University of Essex, 2005.