Evolutionary squeaky wheel optimization: A new framework for analysis

Evolutionary Computation

Volumn 19, Issue 3, 2011, Pages 405-428

  Li, Jingpeng ^a   Parkes, Andrew J ^a   Burke, Edmund K ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Combinatorial optimization; Markov chain; Metaheuristics; Stochastic process; Stochastic search

Indexed keywords

COMPONENTS OF SOLUTIONS; FORMAL FRAMEWORK; HEURISTIC SELECTIONS; LOCAL SEARCH; MARKOV CHAIN; META HEURISTICS; METAHEURISTIC; MONOTONICITY; PROBLEM INSTANCES; REAL-WORLD PROBLEM; SEARCH PROCESS; SEARCH SPACES; SQUEAKY WHEEL OPTIMIZATIONS; STATIONARY DISTRIBUTION; STOCHASTIC PROCESS; STOCHASTIC SEARCH;

COMBINATORIAL OPTIMIZATION; EVOLUTIONARY ALGORITHMS; MARKOV PROCESSES; MATHEMATICAL OPERATORS; PROBABILITY DISTRIBUTIONS; STOCHASTIC SYSTEMS; WHEELS;

SIMULATED ANNEALING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; METHODOLOGY; PROBABILITY; SEARCH ENGINE; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; MARKOV CHAINS; MODELS, THEORETICAL; SEARCH ENGINE;

EID: 80051540479     PISSN: 10636560     EISSN: 15309304     Source Type: Journal    
DOI: 10.1162/EVCO_a_00033     Document Type: Article

References (28)

1. Agapie, A. (1998). Genetic algorithms: Minimal conditions for convergence. Artificial Evolution, Lecture Notes in Computer Science, 1363:181-193.
2. Aickelin, U., Burke, E. K., and Li, J. (2009). An evolutionary squeaky wheel optimisation approach to personnel scheduling. IEEE Transactions on Evolutionary Computation, 13:433-443.
3. Aickelin, U., and Li, J. (2007). An estimation of distribution algorithm for nurse scheduling. Annals of Operations Research, 155:289-309.
4. Barbulescu, L., Watson, J. P., Whitley, L., and Howe, A. E. (2004). Scheduling space-ground communications for the air force satellite control network. Journal of Scheduling, 27:7-34.
5. Burke, E. K., Hyde, M., and Kendall, G. (2010). A squeaky wheel optimisation methodology for two dimensional strip packing. Computers & Operations Research, 14:942-958.
6. Burke, E. K., and Newall, J. P. (2004). Solving examination timetabling problems through adaptation of heuristic orderings. Annals of Operations Research, 129:107-134.
7. Dorigo, M., Maniezzo, V., and Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 26:29-41.
8. Feng, G., and Lau, H. (2008). Efficient algorithms for machine scheduling problems with earliness and tardiness penalties. Annals of Operations Research, 159:83-95.
9. Feo, A., and Resende, M. G. C. (1989). A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters, 8:67-71.
10. Fu, Z., Li, Y., Lim, A., and Rodrigues, B. (2007). Port space allocation with a time dimension. Journal of the Operational Research Society, 58:797-807.
11. Gendreau, M., and Potvin, J. Y. (2010). Handbook of meta-heuristics, 2nd ed. Berlin: Springer.
12. Ginsberg, M. L. (2001). Gib: Imperfect information in a computationally challenging game. Journal of Artificial Intelligence Research, 14:303-358.
13. Glover, F. (1989). Tabu search, Part I. ORSA Journal on Computing, 1:190-206.
14. Goldberg, D. (1989). Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley.
15. Hansen, P., and Mladenović, N. (1999). Variable neighbourhood search: Principles and applications. European Journal of Operational Research, 130:449-467.
16. Hoos, H. H., and Stutzle, T. (2005). Stochastic local search: Foundations and applications. SanMateo, CA: Morgan Kaufmann.
17. Joslin, D., and Clements, D. P. (1999). Squeaky wheel optimization. Journal of Artificial Intelligence Research, 10:353-373.
18. Kirkpatrick, S., Gelatt, C., and Vecchi, M. (1983). Optimization by simulated annealing. Science, 220:671-680.
19. Koralov, L. B., and Sinai, Y. G. (2007). Theory of probability and random processes. Berlin: Springer.
20. Larrañaga, P., and Lozano, J. A. (2002). Estimation of distribution algorithms: A new tool for evolutionary computation. Dordrecht, The Netherlands: Kluwer Academic Publishers.
21. Li, J., and Kwan, R. S. K. (2003). A fuzzy genetic algorithm for driver scheduling. European Journal of Operational Research, 147:334-344.
22. Lim, A., Rodrigues, B., Xiao, F., and Zhu, Y. (2004). Crane scheduling with spatial constraints. Naval Research Logistics, 51:386-406.
23. Malaguti, E., and Toth, P. (2008). An evolutionary approach for bandwidth multicoloring problems. European Journal of Operational Research, 189:638-651.
24. Michiels, W., Aarts, E., and Korst, E. J. (2007). Theoretical aspects of local search. Berlin: Springer.
25. Minton, S., Johnston, M. D., Philips, A. B., and Laird, P. (1992). Minimizing conflicts: A heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence Journal, 58:161-205.
26. Papadimitriou, C. H., and Steiglitz, K. (1982). Combinatorial optimization-Algorithms and complexity. New York: Dover.
27. Rudolph, G. (1994). Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 5:96-101.
28. Tijms, H. C. (2003). A first course in stochastic models. New York:Wiley.