Advertisement

Representation Development from Pareto-Coevolution

  • Edwin D. de Jong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)

Abstract

Genetic algorithms generally use a fixed problem representation that maps variables of the search space to variables of the problem, and operators of variation that are fixed over time. This limits their scalability on non-separable problems. To address this issue, methods have been proposed that coevolve explicitly represented modules. An open question is how modules in such coevolutionary setups should be evaluated.

Recently, Pareto-coevolution has provided a theoretical basis for evaluation in coevolution. We define a notion of functional modularity, and objectives for module evaluation based on Pareto-Coevolution. It is shown that optimization of these objectives maximizes functional modularity. The resulting evaluation method is developed into an algorithm for variable length, open ended development of representations called DevRep. DevRep successfully identifies large partial solutions and greatly outperforms fixed length and variable length genetic algorithms on several test problems, including the 1024-bit Hierarchical-XOR problem.

Keywords

Development of representations hierarchical modularity Pareto-coevolution Evolutionary Multi-Objective Optimization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Peter J. Angeline and Jordan B. Pollack. Coevolving high-level representations. In Christopher G. Langton, editor, Artificial Life III, volume XVII of SFI Studies in the Sciences of Complexity, pages 55–71, Redwood City, CA, 1994. Addison-Wesley.Google Scholar
  2. 2.
    Anthony Bucci and Jordan B. Pollack. Order-theoretic analysis of coevolution problems: Coevolutionary statics. In Proceedings of the GECCO-2002 Workshop on Coevolution: Understanding Coevolution, 2002.Google Scholar
  3. 3.
    Edwin D. De Jong and Tim Oates. A coevolutionary approach to representation development. In E.D. de Jong and T. Oates, editors, Proceedings of the ICML-2002 Workshop on Development of Representations, Sydney NSW 2052, 2002. The University of New South Wales. Online proceedings: http://www.demo.cs.brandeis.edu/icml02ws.
  4. 4.
    Edwin D. De Jong and Jordan B. Pollack. Learning the ideal evaluation function. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2003, 2003.Google Scholar
  5. 5.
    Kalyanmoy Deb. Multi-Objective Optimization Using Evolutionary Algorithms. Wiley & Sons, New York, NY, 2001.zbMATHGoogle Scholar
  6. 6.
    Sevan G. Ficici and Jordan B. Pollack. Pareto optimality in coevolutionary learning. In Jozef Kelemen, editor, Sixth European Conference on Artificial Life, Berlin, 2001. Springer.Google Scholar
  7. 7.
    Frederic Gruau. Neural Network Synthesis Using Cellular Encoding and the Genetic Algorithm. PhD thesis, PhD Thesis, Ecole Normale Supérieure de Lyon, 1994.Google Scholar
  8. 8.
    John R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. The MIT Press, Cambridge, MA, May 1994.zbMATHGoogle Scholar
  9. 9.
    Samir W. Mahfoud. Niching Methods for Genetic Algorithms. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, IL, May 1995. IlliGAL Report 95001.Google Scholar
  10. 10.
    Martin Pelikan and David E. Goldberg. Escaping hierarchical traps with competent genetic algorithms. In L. Spector, E.D. Goodman, A. Wu, W. B. Langdon, H.-M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. H. Garzon, and E. Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001, pages 511–518, San Francisco, CA, 2001. Morgan Kaufmann.Google Scholar
  11. 11.
    Mitchell A. Potter and Kenneth A. De Jong. Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation, 8(1):1–29, 2000.CrossRefGoogle Scholar
  12. 12.
    Justinian P. Rosca and Dana H. Ballard. Discovery of subroutines in genetic programming. In P.J. Angeline and K. E. Kinnear, Jr., editors, Advances in Genetic Programming 2, chapter 9, pages 177–202. The MIT Press, Cambridge, MA, 1996.Google Scholar
  13. 13.
    J. David Schaffer. Multiple objective optimization with vector evaluated genetic algorithms. In John J. Grefenstette, editor, Proceedings of the First International Conference on Genetic Algorithms and their Applications, pages 93–100, Hillsdale, NJ, 1985. Lawrence Erlbaum Associates.Google Scholar
  14. 14.
    Dirk Thierens. Scalability problems of simple genetic algorithms. Evolutionary Computation, 7(4):331–352, 1999.CrossRefGoogle Scholar
  15. 15.
    Kagan Tumer and David Wolpert. Collective intelligence and Braess’ paradox. In Proceedings of the 7th Conference on Artificial Intelligence (AAAI-00) and of the 12th Conference on Innovative Applications of Artificial Intelligence (IAAI-00), pages 104–109, Menlo Park, CA, 2000. AAAI Press.Google Scholar
  16. 16.
    Richard A. Watson. Compositional Evolution: Interdisciplinary Investigations in Evolvability, Modularity, and Symbiosis. PhD thesis, Brandeis University, 2002.Google Scholar
  17. 17.
    Richard A. Watson. Modular interdependency in complex dynamical systems. In Bilotta et al., editor, Workshop Proceedings of the 8th International Conference on the Simulation and Synthesis of Living Systems. UNSW Australia, 2003.Google Scholar
  18. 18.
    Richard A. Watson, Gregory S. Hornby, and Jordan B. Pollack. Modeling building-block interdependency. In A.E. Eiben, Th. Bäck, M. Schoenauer, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature, PPSN-V., volume 1498 of LNCS, pages 97–106, Berlin, 1998. Springer.CrossRefGoogle Scholar
  19. 19.
    Richard A. Watson and Jordan B. Pollack. Symbiotic combination as an alternative to sexual recombination in genetic algorithms. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. Julian Merelo, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature, PPSN-VI, volume 1917 of LNCS, Berlin, 2000. Springer.Google Scholar
  20. 20.
    Richard A. Watson and Jordan B. Pollack. A computational model of symbiotic composition in evolutionary transitions. Biosystems, 69(2–3):187–209, May 2003. Special Issue on Evolvability, ed. Nehaniv.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Edwin D. de Jong
    • 1
  1. 1.DSS GroupUtrecht UniversityThe Netherlands

Personalised recommendations