Analyzing directed acyclic graph recombination

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2206 LNCS, Issue , 2001, Pages 739-748

  Cotta, Carlos ^a   Troya, José M ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; GRAPH THEORY;

DIRECTED ACYCLIC GRAPH (DAG); EDGE DENSITIES; EXPERIMENTAL ANALYSIS; GRAPH SIZES; QUADRATIC COMPLEXITY; RECOMBINATION OPERATORS; TEST PROBLEM; WORK STUDY;

DIRECTED GRAPHS;

EID: 84883787518     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45493-4_72     Document Type: Conference Paper

References (15)

1. K. Chen, J. Cong, Y. Ding, A. Kahng, and P. Trajmar. DAG-Map: Graph-based FPGA technology mapping for delay optimization. IEEE Design and Test of Computers, pages 7-20, September 1992.
2. M. Cosnard and M. Loi. Automatic task graphs generation techniques. Parallel Processing Letters, 5(4):527-538, 1995.
3. C. Cotta and J.M. Troya. On the influence of the representation granularity in heuristic forma recombination. In J. Carroll, E. Damiani, H. Haddad, and D. Oppenheim, editors, ACM Symposium on Applied Computing 2000, pages 433-439. ACM Press, 2000.
4. L.M. de Campos and J.F. Huete. A new approach for learning belief networks using independence criteria. International Journal of Approximate Reasoning, 24(1):11-37, 2000. (Pubitemid 34198912)
5. H. Ehrenburg. Improved direct acyclic graph handling and the combine operator in genetic programming. In J.R. Koza, D.E. Goldberg, D.B. Fogel, and R.L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 285-291, Stanford University, CA, 1996, MIT Press.
6. S. Handley. On the use of a directed acyclic graph to represent a population of computer programs. In Proceedings of the 1994 IEEE World Congress on Computational Intelligence, pages 154-159, Orlando, FL, 1994. IEEE Press.
7. D. Heckerman and M. Wellman. Bayesian networks. Communications of the ACM, 38:27-30, 1995.
8. D. Kuck, R. Kuhn, B. Leasure, D. Padua, and M. Wolfe. Dependence graphs and compiler optimizations. In Proceedings of the Eighth Annual ACM Symposium on Principles of Programming Languages, pages 207-218, 1981.
9. P. Larrañaga, C. Kuijpers, R. Murga, and Y. Yurramendi. Learning bayesian network structures by searching for the best ordering with genetic algorithms. IEEE Transactions on System, Man and Cybernetics, 26(4):487-493, 1996. (Pubitemid 126784548)
10. K. Murphy. An introduction to graphical models. Technical report, Intel Research, May 2001.
11. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, Palo Alto, CA, 1988.
12. R. Poli. Parallel distributed genetic programming. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 403-432. McGraw-Hill, 1999.
13. N.J. Radcliffe. Equivalence class analysis of genetic algorithms. Complex Systems, 5:183-205, 1991.
14. N.J. Radcliffe. The algebra of genetic algorithms. Annals of Mathematics and Artificial Intelligence, 10:339-384, 1994.
15. R. Turner, Song Li, and E. Gobbetti. Metis - an object-oriented toolkit for constructing virtual reality applications. Computer Graphics Forum, 18(2):121-130, 1999.