DECA: Dimension Extracting Coevolutionary Algorithm

GECCO 2006 - Genetic and Evolutionary Computation Conference

Volumn 1, Issue , 2006, Pages 313-320

  De Jong, Edwin D ^a   Bucci, Anthony ^b  

^a UTRECHT UNIVERSITY   (Netherlands)

^b BRANDEIS UNIVERSITY   (United States)

Author keywords

Coevolution; DECA; Dimension extraction; Underlying objectives

Indexed keywords

APPROXIMATION THEORY; COMPUTATIONAL METHODS; FEATURE EXTRACTION; IMAGE COMPRESSION; INVERSE PROBLEMS; ONLINE SYSTEMS;

COEVOLUTION; DIMENSION EXTRACTING COEVOLUTIONARY ALGORITHM (DECA); NUMBERS GAME PROBLEMS; TARTARUS PROBLEMS;

EVOLUTIONARY ALGORITHMS;

EID: 33750239892     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Ashlock, D., Willson, S., & Leahy, N. (2004). Coevolution and tartarus. In Proceedings of the 2004 IEEE Congress on Evolutionary Computation, pp. 1618-1624, Portland, Oregon. IEEE Press.
2. Axelrod, R. (1987). The evolution of strategies in the iterated prisoner's dilemma. In Davis, L. (Ed.), Genetic Algorithms and Simulated Annealing, Research Notes in Artificial Intelligence, pp. 32-41, London. Pitman Publishing.
3. Barricelli, N. A. (1962). Numerical testing of evolution theories. Part I: Theoretical introduction and basic tests. Ada Biotheoretica, 16(1-2), 69-98.
4. Bucci, A., & Pollack, J. B. (2005). On identifying global optima in cooperative coevolution. In Beyer, H.-G., et al. (Ed.), GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, Vol. 1, pp. 539-544, Washington DC, USA. ACM Press.
5. Bucci, A., Pollack, J. B., & De Jong, E. D. (2004). Automated extraction of problem structure. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-04, pp. 501-512.
6. De Jong, E. D. (2004a). The Incremental Pareto-Coevolution Archive. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-04. pp. 525-536.
7. De Jong, E. D. (2004b). Towards a bounded Pareto-Coevolution archive. In Proceedings of the Congress on Evolutionary Computation, CEC-04, pp. 2341-2348.
8. De Jong, E. D. (2005). The MaxSolve algorithm for coevolution. In Beyer, H.-G. (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-05. pp. 483-489. ACM Press.
9. De Jong, E. D., & Pollack, J. B. (2004). Ideal evaluation from coevolution. Evolutionary Computation, 12(1), 159-192.
10. Epstein, S. L. (1994). Toward an ideal trainer. Machine Learning, 15(3), 251-277.
11. Ficici, S. G. (2004). Solution Concepts in Coevolutionary Algorithms. Ph.D. thesis, Brandeis University.
12. Ficici, S. G., & Pollack, J. B. (2000). A game-theoretic approach to the simple coevolutionary algorithm. In Schoenauer, M., et al. (Ed.), Parallel Problem Solving from Nature, PPSN-VI, Vol. 1917 of LNCS, Berlin. Springer.
13. Ficici, S. G., & Pollack, J. B. (2001). Pareto optimality in Coevolutionary learning. In Kelemen, J. (Ed.), Sixth European Conference on Artificial Life, Berlin. Springer.
14. Ficici, S. G., & Pollack, J. B. (2003). A game-theoretic memory mechanism for coevolution. In Cantú-Paz, E., et al. (Ed.), Genetic and Evolutionary Computation - GECCO-2003, Vol. 2723 of LNCS, pp. 286-297, Chicago. Springer-Verlag.
15. Hillis, D. W. (1990). Co-evolving parasites improve simulated evolution in an optimization procedure. Physica D, 42, 228-234.
16. Juillé, H., & Pollack, J. B. (1998). Coevolving the "ideal" trainer: Application to the discovery of cellular automata rules. In Proceedings of the Third Annual Genetic Programming Conference.
17. Rosin, C. D. (1997). Coevolutionary Search among Adversaries. Ph.D. thesis, University of California, San Diego, CA.
18. Teller, A. (1994). The evolution of mental models. In Kinnear, K. E. (Ed.), Advances in Genetic Programming, Complex Adaptive Systems, pp. 199-220, Cambridge. MIT Press.
19. Watson, R. A., & Pollack, J. B. (2000). Symbiotic combination as an alternative to sexual recombination in genetic algorithms. In Schoenauer, M., et al. (Ed.), Parallel Problem Solving from Nature, PPSN-VI, Vol. 1917 of LNCS, Berlin. Springer.
20. Watson, R. A., & Pollack, J. B. (2001). Coevolutionary dynamics in a minimal substrate. In Spector, L., et al. (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-01, pp. 702-709, San Francisco, CA. Morgan Kaufmann.
21. Watson, R. A., & Pollack, J. B. (2003). A computational model of symbiotic composition in evolutionary transitions. Biosystems, 69(2-3), 187-209. Special Issue on Evolvability, ed. Nehaniv.