Dependency structure matrix, genetic algorithms, and effective recombination

Evolutionary Computation

Volumn 17, Issue 4, 2009, Pages 595-626

  Yu, Tian Li ^a   Goldberg, David E ^b   Sastry, Kumara ^b   Lima, Claudio F ^c   Pelikan, Martin ^d  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b University of Illinois at Urbana Champaign   (United States)

^c UNIVERSITY OF ALGARVE   (Portugal)

^d UNIVERSITY OF MISSOURI ST LOUIS   (United States)

Author keywords

Dependency structure matrix; Genetic algorithms; Hierarchy; Modularity; Overlap; Problem decomposition

Indexed keywords

ALGORITHM; ARTICLE; PROBLEM SOLVING;

ALGORITHMS; PROBLEM SOLVING;

EID: 72249091950     PISSN: 10636560     EISSN: 15309304     Source Type: Journal    
DOI: 10.1162/evco.2009.17.4.17409     Document Type: Article

References (76)

1. Barron, A., Rissenen, J., and Yu, B. (1998). The MDL principle in coding and modeling. IEEE Transactions on Information Theory, 44(6):2743-2760.
2. Chen, Y.-P., and Goldberg, D. E. (2005). Convergence time for the linkage learning genetic algorithm. Evolutionary Computation, 13(3):279-302.
3. Christopher, A. (1964). Notes on the synthesis of form. Boston, MA: Harvard Press.
4. Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C. (2001). Introduction to algorithms (2nd ed.). Cambridge, MA: MIT Press.
5. Deb, K., and Goldberg, D. E. (1993). Analyzing deception in trap functions. In Foundations of Genetic Algorithms 2, pp. 93-108.
6. DeJong, E. D., Thierens, D., and Watson, W. (2004). Hierarchical genetic algorithms. In Parallel Problem Solving from Nature (PPSN VIII), pp. 232-241.
7. Descartes, R. (1994). A discourse on the method of rightly conducting the reason, and seeking truth in the sciences [Veitch, J. (trans.)]. In T. Sorell (Ed.), A discourse on method: Meditations and principles (pp. 3-57). London, UK: Everyman. (Original work published 1637.)
8. Eppinger, S. D., Whitney, D. E., Smith, R. P., and Gebala, D. A. (1994). A model-based method for organizing tasks in product development. Research in Engineering Design, 6(1):1-13.
9. Fernandez, C. (1998). Integration analysis of product architecture to support effective team co-location. Masters thesis, Massachusetts Institute of Technology, Cambridge.
10. Fischer, K. H., and Hertz, J. A. (1991). Spin glasses. New York: Cambridge University Press.
11. Galluccio, A., and Loebi, M. (1999a). A theory of Pfaffian orientations. I. Perfect matchings and permunents. Electronic Journal of Combinatorics, 6(1):Research Paper 6.
12. Galluccio, A., and Loebi, M. (1999b). A theory of Pfaffian orientations. II. t-joins, k-cuts, and duality of enumeration. Electronic Journal of Combinatorics, 6(1):Research Paper 7.
13. Goldberg, D. E. (1987). Simple genetic algorithms and the minimal, deceptive problem. In Genetic Algorithms and Simulated Annealing, Chap. 6 (pp. 74-88) Pitman Publishing, London.
14. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.
15. Goldberg, D. E. (2002). The design of innovation: Lessons from and for competent genetic algorithms. Boston, MA: Kluwer Academic Publishers.
16. Goldberg, D. E., Deb, K., and Clark, J. H. (1992). Genetic algorithms, noise, and the sizing of populations. Complex Systems, 6:333-362.
17. Goldberg, D. E., Deb, K., Kargupta, H., and Harik, G. (1993). Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. In Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 56-64.
18. Goldberg, D. E., Korb, B., and Deb, K. (1989). Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems, 3(5):493-530.
19. Harik, G. R. (1994). Finding multiple solutions in problems of bounded difficulty. IlliGAL Report No. 94002, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana.
20. Harik, G. R. (1997). Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms. Doctoral dissertation, University of Michigan, Ann Arbor.
21. Harik, G. R. (1999). Linkage learning via probabilistic modeling in the ecGA. IlliGAL Report No. 99010, University of Illinois at Urbana-Champaign, Urbana, IL.
22. Harik, G. R., Cantú-Paz, E., Goldberg, D. E., and Miller, B. L. (1997). The gambler's ruin problem, genetic algorithms, and the sizing of populations. In Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, pp. 7-12.
23. Harik, G. R., Lobo, F. G., and Goldberg, D. E. (1998). The compact genetic algorithm. In Proceedings of IEEE International Conference on Evolutionary Computation, pp. 523-528.
24. Haynes, T. (1997). Phenotypical building blocks for genetic programming (pp. 26-33). San Mateo, CA: Morgan Kaufmann.
25. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
26. Holland, J. H. (1995). Hidden order-How adaptation builds complexity. Reading, MA: Perseus Books.
27. Holland, J. H. (2000). Building blocks, cohort genetic algorithms, and hyperplane-defined functions. Evolutionary Computation, 8(4):373-391.
28. Hutter, M., and Zaffalon, M. (2005). Distribution of mutual information from complete and incomplete data. Computational Statistics and Data Analysis, 48(3):633-657.
29. Iclanzan, D., and Dumitrescu, D. (2007). Overcoming hierarchical difficulty by hill-climbing the building block structure. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2007), pp. 1256-1263.
30. Kargupta, H. (1996). The performance of the gene expression messy genetic algorithm on real test functions. In Proceedings of 1996 IEEE International Conference on Evolutionary Computation, pp. 631-636.
31. Kleiter, G. D. (1999). The posterior probability of Bayes nets with strong dependences. Soft Computing, 3:162-173.
32. Koza, J. (1994). Genetic programming II: Automatic discovery of reusable programs. Cambridge, MA: MIT Press.
33. Kullback, S., and Leibler, R. A. (1951). On information and sufficiency. Annual Mathematical Statistics, 22:79-86.
34. Larrañaga, P., and Lozano, J. (Eds.). (2002). Estimation of distribution algorithms. Boston, MA: Kluwer Academic Publishers.
35. Lutz, R. (2002). Recovering high-level structure of software systems using a minimum description length principle. In Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science (AICS), pp. 61-69.
36. McCord, K., and Eppinger, S. D. (1993). Managing the integration problem in concurrent engineering. Working Paper 3594, MIT Sloan School of Management, Cambridge, Massachusetts.
37. Miller, B. L. (1997). Noise, sampling, and efficient genetic algorithms. Doctoral dissertation, University of Illinois at Urbana-Champaign, Urbana.
38. Mitchell, M. (1996). An introduction to genetic algorithms. Cambridge, MA: MIT Press.
39. Monien, B., and Sudborough, I. H. (1988). MIN-CUT is NP-complete for edge weighted trees. Theoretical Computer Science, 58(1-3):209-229.
40. M̈uhlenbein, H., Mahnig, T., and Rodriguez, A. O. (1999). Schemata, distributions and graphical models in evolutionary optimization. Journal of Heuristics, 5:215-247.
41. Munetomo, M., and Goldberg, D. E. (1999). Identifying linkage groups by nonlinearity/nonmonotonicity detection. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), 1:433-440.
42. Naudts, B., and Naudts, J. (1998). The effect of spin-flip symmetry on the performance of the simple GA. Parallel Problem Solving from Nature, pp. 67-76.
43. Pelikan, M. (2002). Bayesian optimization algorithm: From single level to hierarchy. Doctoral dissertation, University of Illinois at Urbana-Champaign, Urbana.
44. Pelikan, M. (2005). Hierarchical Bayesian optimization algorithm: Toward a new generation of evolutionary algorithms. New York: Springer-Verlag.
45. Pelikan, M., and Goldberg, D. E. (2001). Escaping hierarchical traps with competent genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 511-518.
46. Pelikan, M., and Goldberg, D. E. (2003). Hierarchical BOA solves Ising spin glasses and MAXSAT. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2003), pp. 1271-1282.
47. Pelikan, M., Goldberg, D.E., and Cantú-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), I:525-532.
48. Pelikan, M., and Mühlenbein, H. (1999). The bivariate marginal distribution algorithm. In Proceedings of Advances in Soft Computing-Engineering Design and Manufacturing, pp. 521-535.
49. Pelikan, M., Ocenasek, J., Trebst, S., Troyer, M., and Alet, F. (2004). Computational complexity and simulation of rare events of Ising spin glasses. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), pp. 36-47.
50. Pelikan, M., Sastry, K., and Cantú-Paz, E. (Eds.). (2006). Scalable optimization via probabilistic modeling from algorithms to applications. New York: Springer-Verlag.
51. Pimmler, T., and Eppinger, S. D. (1994). Integration analysis of product decompositions. In Proceedings of the ASME International Conference on Design Theory and Methodology, DE-68:343-351.
52. Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14:465-471.
53. Rissanen, J. (1999). Hypothesis selection and testing by the MDL principle. Computer Journal, 42:260-269.
54. Russell, S., and Norvig, P. (2003). Planning. In Artificial intelligence: A modern approach (2nd ed.), Chap. 11-12 (pp. 375-461). Upper Saddle River, NJ: Prentice Hall.
55. Sastry, K., O'Reilly, U., Goldberg, D., and Hill, D. (2003). Building block supply in genetic programming. In R. L. Riolo and B. Worzel (Eds.), Genetic programming theory and practice, Chap. 9 (pp. 137-154). Boston, MA: Kluwer.
56. Sharman, D., and Yassine, A. (2004). Characterizing complex product architectures. Systems Engineering Journal, 7(1):35-60.
57. th Design Automation Conference, pp. DAC-34119.
58. Simon, H. A. (1968). The science of artifical. Cambridge, MA: MIT Press.
59. Smith, J., and Fogarty, T. C. (1996). Recombination strategy adaptation via evolution of gene linkage. In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, pp. 826-831.
60. Thebeau, R. (2001). Knowledge management of system interfaces and interactions for product development processes. Masters thesis, Massachusetts Institute of Technology, Cambridge.
61. Thierens, D., and Goldberg, D. E. (1993). Mixing in genetic algorithms. In Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 38-45.
62. Toussaint, M. (2005). Compact genetic codes as a search strategy of evolutionary processes. In Foundations of Genetic Algorithms (FOGA 2005), pp. 75-94.
63. Tsuji, M., Munetomo, M., and Akama, K. (2006). A crossover for complex building blocks overlapping. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006), pp. 1337-1344.
64. van Hoyweghen, C. (2001). Detecting spin-flip symmetry in optimization problems. In Theoretical aspects of evolutionary computing (pp. 423-437). New York: Springer-Verlag.
65. van Hoyweghen, C., Goldberg, D. E., and Naudts, B. (2002). From TwoMax to the Ising model: Easy and hard symmetrical problems. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), pp. 626-633.
66. Watson, R. A., Hornby, G. S., and Pollack, J. B. (1998). Modeling building-block interdependency. In Parallel Problem Solving from Nature (PPSN-V), pp. 97-106.
67. Watson, R. A., and Pollack, J. B. (1999). Hierarchically consistent test problems for genetic algorithms: Summary and additional results. In Late Breaking Papers at the Genetic and Evolutionary Computation Conference, pp. 292-297.
68. Watson, R. A., and Pollack, J. B. (2005). Modular interdependency in complex dynamical systems. Artificial Life, 11(4):445-457.
69. Whitfield, R., Smith, J., and Duffy, A. (2002). Identifying component modules. In Proceedings of the Seventh International Conference on Artificial Intelligence in Design AID02, pp. 571-592.
70. Yassine, A., Jogleker, N., Braha, D., Eppingher, S., and Whitney, D. (2003). Information hiding in product development: The design churn effect. Research in Engineering Design, 14(3):145-161.
71. Yu, T.-L., and Goldberg, D. E. (2004). Quality and efficiency of model building for genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pp. 367-378.
72. Yu, T.-L., and Goldberg, D. E. (2006). Conquering hierarchical difficulty by explicit chunking: Substructural chromosome compression. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006), pp. 1385-1392.
73. Yu, T.-L., Goldberg, D. E., Yassine, A., and Chen, Y.-P. (2003a). Genetic algorithm design inspired by organizational theory: Pilot study of a dependency structure matrix driven genetic algorithm. In Proceedings of Artificial Neural Networks in Engineering (ANNIE 2003), pp. 327-332.
74. Yu, T. -L., Sastry, K., and Goldberg, D. E. (2005). Linkagelearning, overlappingbuildingblocks, and systematic strategy for scalable recombination. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2005), pp. 1217-1224.
75. Yu, T.-L., Yassine, A., and Goldberg, D. E. (2003b). A genetic algorithm for developing modular product architectures. In Proceedings of the ASME 2003 International Design Engineering Technical Conferences, pp. DTM-48657.
76. Yu, T.-L., Yassine, A., and Goldberg, D. E. (2007). An information theoretic method for developing modular architectures using genetic algorithms. Research in Engineering Design, 18:91-109.