Abstract
Coevolution can in principle provide progress for problems where no accurate evaluation function is available. An important open question however is how coevolution can be set up such that progress can be ensured. Previous work has provided progress guarantees either for limited cases or using strict acceptance conditions that can result in stalling. We present a monotonically improving archive for the general asymmetric case of coevolution where learners and tests may be of distinct types, for which any detectable improvement can be accepted into the archive. The Incremental Pareto-Coevolution Archive is demonstrated in experiments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Axelrod, R.: The evolution of strategies in the iterated prisoner’s dilemma. In: Davis, L. (ed.) Genetic Algorithms and Simulated Annealing, London. Research Notes in Artificial Intelligence, pp. 32–41. Pitman Publishing (1987)
Barricelli, N.A.: Numerical testing of evolution theories. Part I: Theoretical introduction and basic tests. Acta Biotheoretica 16(1–2), 69–98 (1962)
Bucci, A., Pollack, J.B.: A mathematical framework for the study of coevolution. In: Foundations of Genetic Algorithms (FOGA 2002), San Francisco, CA, Morgan Kaufmann, San Francisco (2003)
Bucci, A., Pollack, J.B., De Jong, E.D.: Automated extraction of problem structure. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004 (2004)
De Jong, E.D.: Towards a bounded Pareto-Coevolution archive. In: Proceedings of the Congress on Evolutionary Computation, CEC 2004 (2004)
De Jong, E.D., Pollack, J.B.: Learning the ideal evaluation function. In: Cantú-Paz, E., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2003, Berlin, pp. 274–285. Springer, Heidelberg (2003)
De Jong, E.D., Pollack, J.B.: Ideal evaluation from coevolution. Evolutionary Computation 12(2) (2004)
Ficici, S.G., Pollack, J.B.: A game-theoretic approach to the simple coevolutionary algorithm. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, Springer, Heidelberg (2000)
Ficici, S.G., Pollack, J.B.: Pareto optimality in coevolutionary learning. In: Kelemen, J. (ed.) Sixth European Conference on Artificial Life, Berlin, Springer, Heidelberg (2001)
Ficici, S.G., Pollack, J.B.: A game-theoretic memory mechanism for coevolution. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 286–297. Springer, Heidelberg (2003)
Hillis, D.W.: Co-evolving parasites improve simulated evolution in an optimization procedure. Physica D 42, 228–234 (1990)
Juillé, H.: Methods for Statistical Inference: Extending the Evolutionary Computation Paradigm. PhD thesis, Brandeis University (1999)
Pagie, L., Hogeweg, P.: Evolutionary consequences of coevolving targets. Evolutionary Computation 5(4), 401–418 (1998)
Paredis, J.: Coevolutionary computation. Artificial Life 2(4) (1996)
Potter, M.A., De Jong, K.A.: Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29 (2000)
Rosin, C.D.: Coevolutionary Search among Adversaries. PhD thesis, University of California, San Diego, CA (1997)
Schmitt, L.M.: Theory of coevolutionary genetic algorithms. In: Guo, M. (ed.) ISPA 2003. LNCS, vol. 2745, pp. 285–293. Springer, Heidelberg (2003)
Stanley, K.O., Miikkulainen, R.: The dominance tournament method of monitoring progress in coevolution. In: Barry, A.M. (ed.) GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, New York, July 8, pp. 242–248. AAAI, Menlo Park (2002)
Watson, R.A.: Compositional Evolution: Interdisciplinary Investigations in Evolvability, Modularity, and Symbiosis. PhD thesis, Brandeis University (2002)
Watson, R.A., Pollack, J.B.: Symbiotic combination as an alternative to sexual recombination in genetic algorithms. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, Springer, Heidelberg (2000)
Paul Wiegand, R.: An Analysis of Cooperative Coevolutionary Algorithms. PhD thesis, George Mason University, Fairfax, Virginia (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Jong, E.D. (2004). The Incremental Pareto-Coevolution Archive. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24854-5_55
Download citation
DOI: https://doi.org/10.1007/978-3-540-24854-5_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22344-3
Online ISBN: 978-3-540-24854-5
eBook Packages: Springer Book Archive