Empirical Modelling of Genetic Algorithms

Evolutionary Computation

Volumn 9, Issue 4, 2001, Pages 461-493

  Myers, Richard ^a   Hancock, Edwin R ^a  

^a UNIVERSITY OF YORK   (United Kingdom)

Author keywords

Constraint satisfaction; Empirical models; Factorial experiments; Genetic algorithms; Line labelling

Indexed keywords

PARAMETER ESTIMATION;

ALGORITHM PARAMETERS; CONSTRAINT SATISFACTION; EMPIRICAL MODEL; FACTORIAL EXPERIMENT; LABELINGS; LATIN SQUARE; LINE-LABELING; LOGISTICS REGRESSIONS; OPTIMAL ALGORITHM; PARAMETER-SETTING;

GENETIC ALGORITHMS;

ALGORITHM; ARTICLE; FACTORIAL ANALYSIS; MATHEMATICAL COMPUTING; STATISTICAL MODEL;

ALGORITHMS; FACTOR ANALYSIS, STATISTICAL; MODELS, STATISTICAL; NUMERICAL ANALYSIS, COMPUTER-ASSISTED;

EID: 0035653516     PISSN: 10636560     EISSN: 15309304     Source Type: Journal    
DOI: 10.1162/10636560152642878     Document Type: Article

References (72)

1. Ackley, D. H. (1987). A Connectionist Machine for Genetic Hillclimbing. Kluwer Academic, Dordtrecht, The Netherlands.
2. Andrey, P. and Tarroux, P. (1994). Unsupervised image segmentation using a distributed genetic algorithm. Pattern Recognition, 27:659–673.
3. Anscombe, F. J. (1953). Contribution to discussion of a paper by H. Hotelling. Journal of the Royal Statistical Society, B15:229–230.
4. Aranda-Ordaz, F. J. (1981). On two families of transformations to additivity for binary response data. Biometrika, 68:357–363.
5. Bedau, M. (1995). Three illustrations of artificial life’s working hypothesis. Lecture Notes in Computer Science, 899:53–68.
6. Bhandarkar, S. M., Zhang, Y., and Potter, W. D. (1994). An edge extraction technique using genetic algorithm-based optimization. Pattern Recognition, 27:1159–1180.
7. Bramlette, M. F. (1991). Initialization, mutation and selection methods in genetic algorithms. In Belew R. K. and Booker, L. B., editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 100–107, Morgan Kaufmann, San Mateo, California.
8. Bremermann, H. J. (1958). The evolution of intelligence. The nervous system as a model of its environment. Technical Report 477(17), Department of Mathematics, University of Washington, Seattle, Washington.
9. Clowes, M. B. (1971). On seeing things. Artificial Intelligence, 2:79–116.
10. Cochran, W. G. and Cox, G. M. (1957). Experimental Designs, Second Edition. John Wiley and Sons, New York, New York.
11. Collett, D. (1991). Modelling Binary Data. Chapman and Hall, London, UK.
12. Cook, S. A. (1971). The complexity of theorem proving procedures. In Proceedings of the Third ACM Symposium on the Theory of Computing, pages 151–158, Association of Computing Machinery, New York, New York.
13. Cross, A. D. J., Wilson, R. C., and Hancock, E. R. (1997). Inexact graph matching using genetic search. Pattern Recognition, 30:953–970.
14. Cross, A. D. J., Myers, R., and Hancock, E. R. (2000). Convergence of a hill-climbing genetic algorithm. Pattern Recognition, 33:1863–1880.
15. Davis, L. (1989). Adapting operator probabilities in genetic algorithms. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 61–69, Morgan Kaufmann, San Mateo, California.
16. Davis, L. S. (1991). A Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, New York.
17. DeJong, K. A. (1975). An Analysis of the Behaviour of a Class of Genetic Adaptive Systems. Ph.D. Thesis, Department of Computer and Communication Sciences, University of Michigan, Ann Arbor, Michigan.
18. Eshelman, L. J. (1991). The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In Rawlins, G. J. E., editor, Foundations of Genetic Algorithms, Volume 1, pages 265–283, Morgan Kaufmann, San Mateo, California.
19. Faugeras, O. D. and Berthod, M. (1981). Improving consistency and reducing ambiguity in stochastic labeling: An optimisation approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3:412–424.
20. Fogel, D. (1994). An introduction to simulated evolutionary optimisation. IEEE Transactions on Neural Networks, 5:3–14.
21. Francis, B., Green, M., and Payne, C., editors (1993). The GLIM System Release 4 Manual. Oxford University Press, Oxford, UK.
22. Fraser, A. S. (1957). Simulation of genetic systems by automatic digital computers. Australian Journal of Biological Science, 10:484–491.
23. Fu, K. S. (1983). A step towards unification of syntactic and statistical pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5:200–205.
24. Garey, M. R. and Johnson, D. S. (1979). Computers and Intractability. Freeman, San Francisco, California.
25. Geiger, D. and Girosi, F. (1991). Parallel and deterministic algorithms from MRFs: Surface reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13:401–412.
26. Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721– 741.
27. Goldberg, D. (1989). Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, Reading, Massachusetts.
28. Goldberg, D. E. and Richardson, J. (1987). Genetic algorithms with sharing for multimodal function optimization. In Grefenstette, J., editor, Proceedings of the Second International Conference on Genetic Algorithms, pages 41–49, Lawrence Erlbaum, Hillsdale, New Jersey.
29. Grefenstette, J. J. (1986). Optimisation of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 16:122–128.
30. Hancock, E. R. (1994). An optimisation approach to line labelling. In Impedovo, S., editor, Progress in Image Analysis and Processing, Volume 3, pages 159–165, World Scientific, Singapore.
31. Hancock, E. R. and Kittler, J. (1990a). Discrete relaxation. Pattern Recognition, 23:711–733.
32. Hancock, E. R. and Kittler, J. (1990b). Edge labelling using dictionary-based relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12:165–181.
33. Haralick, R. M. and Shapiro, L. G. (1979). The consistent labelling problem: Part 1. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1:173–184.
34. Haralick, R. M. and Shapiro, L. G. (1980). The consistent labelling problem: Part 2. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2:193–203.
35. Haralick, R. M., Davis, L. S., and Rosenfeld, A. (1978). Reduction operations for constraint satisfaction. Information Science, 14:199–219.
36. Hays, W. L. (1994). Statistics, Fifth Edition. Harcourt Brace Harcourt Brace, Fort Worth, Texas.
37. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, Massachusetts.
38. Huffman, D. A. (1971). Impossible objects as nonsense sentences. In Meltzer, B. and Michie, D., editors, Machine Intelligence, Volume 6, pages 295–323, Edinburgh University Press, Edinburgh, Scotland.
39. Hummel, R. A. and Zucker, S. W. (1983). On the foundations of relaxation labeling processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5:267–287.
40. Jeavons, P. (1998). On the algebraic structure of combinatorial problems. Theoretical Computer Science, 200:185–204.
41. Jefferson, D. et al. (1991). Evolution as a theme in artificial life: The genesys/tracker system. In Langton, C. G. et al., editors, Artificial Life II. Addison-Wesley, Reading, Massachusetts.
42. Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). Optimisation by simulated annealing. Science, 220:671–680.
43. Koza, J. R. (1992). Genetic Programming. MIT Press, Cambridge, Massachusetts.
44. Lipson, H. and Shpitalni, M. (1996). Optimization-based reconstruction of a 3D object from a single freehand line drawing. CAD, 28:651–663.
45. Lloyd, S. A. (1983). An optimisation approach to relaxation labelling algorithms. Image and Vision Computing, 1:85–91.
46. Mackworth, A. K. (1977). Consistency in networks of relations. Artificial Intelligence, 8:99–118.
47. Mackworth, A. K. and Freuder, E. C. (1985). The complexity of some polynomial network consistency algorithms. Artificial Intelligence, 25:65–74.
48. McCullagh, P. and Nelder, J. A. (1989). Generalised Linear Models, Second Edition. Chapman and Hall, London, UK.
49. Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press, Cambridge, Massachusetts.
50. Mohammed, J. L., Hummel, R. A., and Zucker, S. W. (1983). A gradient projection algorithm for relaxation methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5:330–332.
51. Mühlenbein, H. (1994). Genetic algorithms. In Aarts, E. and Lenstra, J. K., editors, Local Search in Combinatorial Optimisation. Wiley, Bognor Regis, UK.
52. Mühlenbein, H. and Schlierkamp-Voosen, D. (1995). Analysis of selection, mutation and recombination in genetic algorithms. Lecture Notes in Computer Science, 899:142–168.
53. Nudel, B. (1983). Consistent labelling problems and their algorithms. Artificial Intelligence, 21:135– 178.
54. Press, S. A. et al. (1992).Numerical Recipes in C, Second Edition. Cambridge University Press, Cambridge, UK.
55. Prügel-Bennett, A. and Shapiro, J. L. (1994). An analysis of genetic algorithms using statistical physics. Physical Review Letters, 72:1305–1309.
56. Rechenberg, I. (1973). Evolutionsstrategie-Optimierung Technischer Systeme nach Prinzipien der biol-ogischen Information. Fromman Verlag, Stuttgart, Germany.
57. Reed, J., Toombs, R., and Barricelli, N. A. (1967). Simulation of biological evolution and machine learning. Journal of Theoretical Biology, 17:319–342.
58. Rosenfeld, A., Hummel, R. A., and Zucker, S. W. (1976). Scene labelling by relaxation operations. IEEE Transactions on Systems, Man and Cybernetics, 6:420–433.
59. Rudolph, G. (1997). Convergence Properties of Evolutionary Algorithms. Kovac, Hamburg, Germany.
60. Saito, H. and Mori, M. (1995). Application of genetic algorithms to stereo matching of images. Pattern Recognition Letters, 16:815–822.
61. Schaffer, J. D. et al. (1989).A study of control parameters affecting online performance of genetic algorithms for function optimisation. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 51–60, Morgan Kaufmann, San Mateo, California.
62. Schwefel, H. (1981). Numerical Optimization of Computer Models. Wiley, Bognor Regis, UK.
63. Shewchuk, J. R. (1996). Triangle: Engineering a 2D quality mesh generator and Delaunay triangu-lator. In Proceedings of the First Workshop on Applied Computational Geometry, pages 124–133, Association for Computing Machinery, New York, New York.
64. Syswerda, G. (1989). Uniform crossover in genetic algorithms. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 2–9, Morgan Kaufmann, San Mateo, California.
65. Tsang, P. W. M. (1997). A genetic algorithm for affine invariant recognition of object shapes from broken boundaries. Pattern Recognition Letters, 18:631–639.
66. Vose, M. D. (1995). Modelling simple genetic algorithms. Evolutionary Computation, 3:453–472.
67. Waltz, D. (1975). Understanding line drawings of scenes with shadows. In Winston, P. H., editor, The Psychology of Computer Vision, pages 19–91, McGraw-Hill, London, UK.
68. Whitley, D. (1989). The GENITOR algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 116–121, Morgan Kaufmann, San Mateo, California.
69. Whitley, D. et al. (1995). Test driving three 1995 genetic algorithms: New test functions and geometric matching. Journal of Heuristics, 1:77–104.
70. Wilkinson, G. N. and Rogers, C. E. (1973). Symbolic description of factorial models for analysis of variance. Applied Statistics, 22:392–399.
71. Wilson, R. C. and Hancock, E. R. (1997). Structural matching by discrete relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:634–648.
72. Yuille, A. L. and Kosowsky, J. J. (1994). Statistical physics algorithms that converge. Neural Computation, 6:341–356.