Comparison of a genetic algorithm and simulated annealing in an application to statistical image reconstruction

Statistics and Computing

Volumn 7, Issue 3, 1997, Pages 193-207

  Franconi, Luisa ^a   Jennison, Christopher ^b  

^a ITALIAN NATIONAL INSTITUTE OF STATISTICS   (Italy)

^b UNIVERSITY OF BATH   (United Kingdom)

Author keywords

Crossover; Genetic algorithms; Hybrid algorithms; MAP image estimation; Simulated annealing

Indexed keywords

EID: 0042230281     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1018538202952     Document Type: Article

References (46)

1. Atkinson, A. C. (1992) A segmented algorithm for simulated annealing. Statistics and Computing, 2, 221-30.
2. Atkinson, A. C. and Weisberg, S. (1991) Simulated annealing for the detection of multiple outliers using least squares and least median of squares fitting. In Directions in Robust Statistics and Diagnostics, Part I, (W. Stahel and S. Weisberg, eds), Springer-Verlag, New York, pp. 7-20.
3. Battiti, R. and Tecchiolli, G. (1992) Parallel biased search for combinatorial optimization: genetic algorithms and TABU. Microprocessors and Microsystems, 16, 351-67.
4. Besag, J. E. (1986) On the statistical analysis of dirty pictures (with discussion). Journal of the Royal Statistical Society B, 48, 259-302.
5. Boseniuk, T. and Ebeling, W. (1991) Boltzmann-, Darwin-, and Haeckel-strategies in optimization problems. In Lecture Notes in Computer Sciences: Parallel Problem Solving from Nature, 496, pp. 430-44.
6. Brown, D. E., Huntley, C. L. and Spillane, A. R. (1989) A parallel genetic heuristic for the quadratic assignment problem. In Proceedings of the Third International Conference on Genetic Algorithms, (J. D. Schaffer, ed.), Morgan Kaufmann, San Mateo, California, pp. 406-15.
7. Bui, T. N. and Moon, B. R. (1995) On multi-dimensional encoding/crossover. In Proceedings of the Sixth International Conference on Genetic Algorithms, (L. J. Eshelman, ed.), Morgan Kaufmann, San Mateo, California, pp. 49-56.
8. Crawford, K. D. and Wainwright, R. L. (1995) Applying genetic algorithms to outlier detection. In Proceedings of the Sixth International Conference on Genetic Algorithms, (L. J. Eshelman, ed.), Morgan Kaufmann, San Mateo, California, pp. 546-550
9. Das, R. and Whitley, D. (1991) The only challenging problems are deceptive: Global search by solving order-1 hyperplanes. In Proceedings of the Fourth International Conference on Genetic Algorithms, (R. K. Belew and L. B. Booker, eds), Morgan Kaufmann, San Mateo, California, pp. 166-73.
10. Davis, L. (1991) Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York.
11. De Jong, K. A. and Spears, W. M. (1991) An analysis of the interacting role of population size and cross-over in genetic algorithms. In Lecture Notes in Computer Sciences, Parallel Problem Solving from Nature, 496, (H. P. Schwefel and R. Manner, eds), Springer-Verlag, London, pp. 38-47.
12. DeJong, K. and Spears, W. (1993) On the state of evolutionary computation. In Proceedings of the Fifth International Conference on Genetic Algorithms, (S. Forrest ed.), Morgan Kaufmann, San Mateo, California, pp. 618-23.
13. Eshelman, L. J. (1991) The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In Foundations of Genetic Algorithms and Classifier Systems, (G. Rawlins, ed.), Morgan Kaufmann, San Mateo, California, pp. 265-83.
14. Eshelman, L. J and Schaffer, J. D. (1991) Preventing premature convergence in genetic algorithms by preventing incest. In Proceedings of the Fourth International Conference on Genetic Algorithms, (R. K. Belew and L. B. Booker, eds), Morgan Kaufmann, San Mateo, California, pp. 115-22.
15. Geman, D. and Geman, S. (1984) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-41.
16. Goldberg, D. E. (1989) Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Reading, Mass.
17. Goldberg, D. E. (1990) A note on Boltzmann tournament selection for genetic algorithms and population-oriented simulated anealing. Complex Systems, 4, 445-60.
18. Goldberg, D. E. and Richardson, J. (1987) Genetic algorithms with sharing for multimodal function optimization. In Proceedings of the Second International Conference on Genetic Algorithms, (J. J. Grefenstette, ed.), Lawrence Erlbaum, Hillsdale, NJ, pp. 41-9.
19. Goutsias, J. (1989) A theoretical analysis of Monte Carlo algorithm for the simulation of Gibbs random field images. Technical Report JHU/ELE 89-07, Image Analysis and Communications Laboratories, Dept of Elect. and Comput. Eng., The John Hopkins University, Baltimore.
20. Goutsias, J. (1991) A theoretical analysis of Monte Carlo algorithm for the simulation of Gibbs random field images. IEEE Transactions on Information Theory, 373, 1618-28.
21. Grefenstette, J. J. (1987) Incorporating problem specific knowledge into genetic algorithms. In Genetic Algorithms and Simulated Annealing, (L. Davis, ed.), Pitman, London, pp. 42-60.
22. Greig, D. M., Porteous, B. T. and Seheult, A. H. (1989) Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society B, 51, 271-9.
23. Hatjimihail, A. T. and Hatjimihail, T. T. (1995) Design of statistical quality control procedures using genetic algorithms. In Proceedins of the Sixth International Conference on Genetic Algorithms, (L. J. Eshelman, ed.), Morgan Kaufmann, San Mateo, California, pp. 551-7.
24. Holland, J. H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.
25. Holland, J. H. (1992) Genetic Algorithms. Scientific American, 267, 44-50.
26. Ingber, L. and Rosen, B. (1992) Genetic algorithms and very fast simulated reannealing: A comparison. Mathematical and Computer Modelling, 16, 87-100.
27. Jennison, C. and Sheehan, N. (1995) Theoretical and empirical properties of the genetic algorithm as a numerical optimizer. Journal of Computational and Graphical Statistics, 4, 296-318.
28. Julstrom, B. A. (1995) What have you done for me lately? Adapting operator probabilities in a steady-state genetic algorithm. In Proceedings of the Sixth International Conference on Genetic Algorithms, (L. J. Eshelman, ed.), Morgan Kaufmann, San Mateo, California, pp. 81-7.
29. Kido, T., Kitano, H. and Nakanishi, M. (1993) A hybrid search for genetic algorithms: Combining genetic algorithms, TABU search, and simulated annealing. In Proceedings of the Fifth International Conference on Genetic Algorithms, (S. Forrest, ed.), Morgan Kaufmann, San Mateo, California, p. 641.
30. Kirkpatrick, S., Gelatt, C. D. and Vecchi, M. P. (1983) Optimization by simulated annealing. Science, 220, 671-80.
31. Lin, F.-T., Kao, C.-Y. and Hsu, C.-C. (1991) Incorporating genetic algorithms into simulated annealing. In Proceedings of the Fourth International Symposium on AI, pp. 290-297.
32. Mahfoud, S. W. and Goldberg, D. E. (1995) Parallel recombinative simulated annealing: A genetic algorithm. Parallel Computing, 21, 1-28.
33. Michalewicz, Z. (1994) Non-standard methods in evolutionary computation. Statistics and Computing 4, 141-55.
34. Michalewicz, Z. and Janikow, C. Z. (1991) Genetic algorithms for numerical optimization. Statistics and Computing, 1, 75-91.
35. Michalewicz, Z., Vignaux, G. A. and Hobbs, M. (1991) A non-standard genetic algorithm for the nonlinear transportation problem. ORSA Journal on Computing, 3, 307-16.
36. Reeves, C. R. and Wright, C. C. (1995a) Epistasis in genetic algorithms: An experimental design perspective. In Proceedings of the Sixth International Conference on Genetic Algorithms, (L. J. Eshelman, ed.), Morgan Kaufmann, San Mateo, California pp. 217-24.
37. Reeves, C. R. and Wright, C. C. (1995b) An experimental design perspective on genetic algorithms. In Foundations of Genetic Algorithms 3, (L. J. Whitley and M. D. Vose, eds), Morgan Kaufmann, San Francisco, California, pp. 7-22.
38. Reeves, C. R. and Wright, C. C. (1995c) Genetic algorithms and statistical methods: a comparison. In Genetic Algorithms in Engineering System: Innovations and Applications, IEE Conference Publication No. 414, pp. 137-40.
39. South, M. C., Wetherill, G. B. and Tham, M. T. (1993) Hitchhiker's guide to genetic algorithms. Journal of Applied Statistics, 20, 153-75.
40. Stander, J. and Silverman, B. W. (1994) Temperature schedules for simulated annealing. Statistics and Computing, 4, 21-32.
41. Tan, K. C., Li, Y., Murray-Smith, D. J. and Sharman, K. C. (1995) System identification and linearisation using genetic algorithms with simulated annealing. In Genetic Algorithms in Engineering Systems: Innovations and Applications. IEE Conference Publication No. 414, pp. 164-9.
42. Varanelli, J. M. and Cohoon, J. P. (1995) Population-oriented simulated annealing: A genetic/thermodynamic hybrid approach to optimization. In Proceedings of the Sixth International Conference on Genetic Algorithms. (L. J. Eshelman, ed.), Morgan Kaufmann, San Mateo, California, pp. 174-81.
43. Whitley, D. (1989) The GENITOR algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In Proceedings of the Third International Conference on Genetic Algorithms, (J. D. Schaffer, ed.), Morgan Kaufmann, San Mateo, California, pp. 116-21.
44. Whitley, D. (1994) A genetic algorithm tutorial. Statistics and Computing, 4, 65-85.
45. Wichmann, B. A. and Hill, J. D. (1982) Algorithm AS183. An efficient and portable pseudo-random number generator. Applied Statistics, 31, 188-90.
46. Wu, C. F. J., Mao, S. S. and Ma, F. S. (1990) SEL: A search method based on orthogonal arrays. In Statistical Design and Analysis of Industrial Experiments, (S. Ghosh ed.), Marcel Dekker, New York, pp. 279-310.