Hybrid genetic algorithms and random linkage

Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002

Volumn 2, Issue , 2002, Pages 1733-1738

  Joines, Jeffrey A ^a   Kay, Michael G ^b  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

^b North Carolina State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC ALGORITHMS;

ADAPTIVE MEMORY; COMPUTATION EFFORT; HYBRID GENETIC ALGORITHMS; LOCAL SEARCH; SEARCH SPACES;

ALGORITHMS;

EID: 4544365780     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2002.1004504     Document Type: Conference Paper

References (17)

1. R. S. Barr, B. L. Golden, J. P. Kelly, M.G.C. Resende, and W. R. Stewart Jr. Designing and reporting on computational experiments with heuristic methods. Journal Of Heuristics, 1(1):9-32, 1995.
2. P.C. Chu and J.E. Beasley. A genetic algorithm for the generalised assignment problem. Technical report, The Management School Imperial College London, 1995.
3. E Glover. Tabu search - part I. ORSA Journal of Computing, 3:190-206, 1989.
4. E Glover. Tabu search - part II. ORSA Journal of Computing, 1:4-32, 1990.
5. J. N. Hooker, Testing heuristics: We have it all wrong, Journal of Heuristics 1(1) 33-42:1995
6. C.R. Houck, J.A. Joines, and M.G. Kay. Comparison of genetic algorithms, random restart, and two-opt switching for solving large location-allocation problems. Computers & Operations Research, 23(6):587-596, 1996.
7. C. R. Houck, J. A. Joines, and M. G. Kay. Empirical investigation of the benefits of partial lamarckianism. Evolutionary Computation, 5(1):31-60, 1997.
8. SAS Institute Inc. SAYSTAT User's Guide. Cary, NC, 4th edition, 1990.
9. J.A. Joines, M.G. Kay, and C.R. Houck. Characterizing search spaces for tabu search and including adaptive memory into a genetic algorithm. Journal of the Chinesse Institute of Industrial Engineers, 17(5):527-536,2000
10. J.A. Joines, M.G. Kay, R. E. King, and C.T. Culbreth. A hybrid genetic algorithm for manufacturing cell design. Journal of the Chinesse Institute of Industrial Engineers, 17(5):549-564, 2000.
11. J.A. Joines andM.G. Kay. Utilizing Hybrid Genetic Algorithms, chapter 8, pages 199-227. Kluwer Academic Publishers, 2002.
12. C. Lawrence, J.L. Zhou, and A.L. Tits. User's guide for cfsqp version 2.4: A c code for solving (large scale) constrained nonlinear (minimax) optimization problems. TR 94016r1, University of Maryland, Electrical Engineering Dept and Institute for Systems Research
13. M. Locatelli and E Schoen. Random linkage: A family of acceptancdrejection algorithms for global optimization. Mathematical Programming, 85(2):379-396, 1999.
14. Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. AI Series. Springer-Verlag, New York, 3rd edition, 1996.
15. J.-M. Renders and S. Flasse. Hybrid methods using genetic algorithms for global optimization. IEEE Transactions on Systems, Man, and Cybernetics Part B:, 26(2):243-258, April 1996.
16. A. Rinnooy-Kan and G. Timmer. Stochastic global optimization methods part i. Mathematical Programming, 39( 1):27-56, 1987.
17. A.A. Zhigljavsky. Theory of Gloval Random Search, volume 65 of Mathematics and Its Applications. Kluwer, Boston, 1991.