A comparison of evaluation methods in coevolution

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 479-487

  Yo, Ting Shuo ^a   De Jong, Edwin D ^a  

^a UTRECHT UNIVERSITY   (Netherlands)

Author keywords

Coevolution; Evaluation; Objective fitness corelation; OFC; Performance comparison

Indexed keywords

CASE BASED REASONING; CORRELATION METHODS; FUNCTION EVALUATION; MULTIOBJECTIVE OPTIMIZATION;

COEVOLUTION; MAJORITY FUNCTION PROBLEMS; OBJECTIVE FITNESS CORRELATION (OFC); PERFORMANCE COMPARISON;

EVOLUTIONARY ALGORITHMS;

EID: 34548066618     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277060     Document Type: Conference Paper

References (20)

1. A. Bucci and J. B. Pollack. A mathematical framework for the study of coevolution. In Foundations of Genetic Algorithms (FOGA-2002), San Francisco, CA, 2003. Morgan Kaufmann.
2. E. D. De Jong. The MaxSolve algorithm for coevolution. In H.-G. Beyer, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-05, pages 483-489. ACM Press, 2005.
3. E. D. De Jong. A monotonie archive for pareto-coevolution. Evolutionary Computation, 15(1), 2007. to appear.
4. E. D. De Jong. Objective fitness correlation. In Dirk Thierens et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-07. ACM Press, 2007.
5. E. D. De Jong and J. B. Pollack. Ideal evaluation from coevolution. Evol Comput., 12(2):159-192, 2004.
6. S. G. Ficici. Solution Concepts in Coevolutionary Algorithms. PhD thesis, Brandeis University, 2004.
7. S. G. Ficici, O. Melnik, and J. B. Pollack. A game-theoretic and dynamical-systems analysis of selection methods in coevolution. IEEE Transactions on Evolutionary Computation, 9(6):580-602, 2005.
8. S. G. Ficici and J. B. Pollack. Pareto optimality in coevolutionary learning. In ECAL '01: Proceedings of the 6th European Conference on Advances in Artificial Life, pages 316-325, London, UK, 2001. Springer-Verlag.
9. S. G. Ficici and J. B. Pollack. A game-theoretic memory mechanism for coevolution. In Cantú-Paz, E., et al, editor, Genetic and Evolutionary Computation - GECCO-2003, volume 2723 of LNCS, pages 286-297, Chicago, 12-16 July 2003. Springer-Verlag.
10. D. W. Hillis. Co-evolving parasites improve simulated evolution in an optimization procedure. Physica D, 42:228-234, 1990.
11. H. Juillé and J. Pollack. Dynamics of co-evolutionary learning. In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 526-534. MIT Press, 1996.
12. H. Juillé and J. B. Pollack. Coevolving the "ideal" trainer: Application to the discovery of cellular automata rules. In Proceedings of the Third Annual Genetic Programming Conference, 1998.
13. J. R. Koza. Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA, USA, 1992.
14. K. M. Lee, H. Xu, and H. F. Chau. Parity problem with a cellular automaton solution. Phys. Rev. E, 64(2):026702, Jul 2001.
15. S. Luke. ECJ 15: A Java evolutionary computation library, http://cs.gmu.edu/~eclab/projects/ecj/, 2006.
16. M. Mitchell, J. P. Crutchfield, and P. T. Hraber. Evolving cellular automata to perform computations: mechanisms and impediments. Phys. D, 75(1-3):361-391, 1994.
17. M. Mitchell, P. T. Hraber, and J. P. Crutchfield. Revisiting the edge of chaos: Evolving cellular automata to perform computations. Complex Systems, 7:89-130, 1993.
18. L. Pagie and P. Hogeweg. Evolutionary consequences of coevolving targets. Evolutionary Computation, 5(4):401-418, 1998.
19. J. Paredis. Coevolutionary computation. Artificial Life, 2(4), 1996.
20. C. D. Rosin and R. K. Belew. New methods for competitive coevolution. Evolutionary Computation, 5(1):1-29, 1997.