Advertisement

Competition-Based Learning

  • John J. Grefenstette
  • Kenneth A. De Jong
  • William M. Spears
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 195)

Abstract

This paper summarizes recent research on competition-based learning procedures performed by the Navy Center for Applied Research in Artificial Intelligence at the Naval Research Laboratory. We have focused on a particularly interesting class of competition-based techniques called genetic algorithms. Genetic algorithms are adaptive search algorithms based on principles derived from the mechanisms of biological evolution. Recent results on the analysis of the implicit parallelism of alternative selection algorithms are summarized, along with an analysis of alternative crossover operators. Applications of these results in practical learning systems for sequential decision problems and for concept classification are also presented.

Keywords

Genetic Algorithm Fitness Function Selection Algorithm Crossover Operator Genetic Algo 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baker, J. E. (1987). Reducing bias and inefficiency in the selection algorithm. Proceedings of the Second International Conference Genetic Algorithms and Their Applications (pp. 14–21). Cambridge, MA: Erlbaum.Google Scholar
  2. Baker, J. E. (1989). Analysis of the effects of selection in genetic algorithms, Doctoral dissertation, Department of Computer Science, Vanderbilt University, Nashville.Google Scholar
  3. Cobb, H. G. and J. J. Grefenstette (1991). Learning the persistence of actions in reactive control rules. Proceedings of the Eighth International Machine Learning Workshop (pp. 293–297). Evanston, IL: Morgan KaufmannGoogle Scholar
  4. De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems. Doctoral dissertation, Department of Computer and Communication Sciences, University of Michigan, Ann Arbor.Google Scholar
  5. De Jong, K. A. (1990). Genetic-algorithm-based learning. In Machine Learning: An artificial intelligence approach, Vol. 3, Y. Kodratoff and R. Michalski (eds.), Morgan Kaufmann.Google Scholar
  6. De Jong, K. A. and W. M. Spears (1992). A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of Mathematics and Artificial Intelligence.Google Scholar
  7. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading: Addison-Wesley.MATHGoogle Scholar
  8. Gordon, D. F. (1991a). An enhancer for reactive plans. Proceedings of the Eighth International Machine Learning Workshop (pp. 505–508). Evanston, IL: Morgan Kaufmann.Google Scholar
  9. Gordon, D. F. (1991b). Improving the comprehensibility, accuracy, and generality of reactive plans. Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems (pp. 358–367). Charlotte, NC: Springer-Verlag.Google Scholar
  10. Grefenstette, J. J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, SMC-16(1), 122–128.CrossRefGoogle Scholar
  11. Grefenstette, J. J. (1988). Credit assignment in rule discovery system based on genetic algorithms. Machine Learning 3(2/3), 225–245.CrossRefGoogle Scholar
  12. Grefenstette, J. J. (1991a). Conditions for implicit parallelism. In Foundations of Genetic Algorithms, G. J. E. Rawlins (ed.), Bloomington, IN: Morgan Kaufmann.Google Scholar
  13. Grefenstette, J. J. (1991b). Lamarckian learning in multi-agent environments. Proceedings of the Fourth International Conference of Genetic Algorithms (pp. 303–310). San Diego, CA: Morgan Kaufmann.Google Scholar
  14. Grefenstette, J. J. and H. G. Cobb (1991). User’s guide for SAMUEL, Version 1.3. NRL Memorandum Report 6820. Washington, DC.Google Scholar
  15. Grefenstette, J. J., C. L. Ramsey and A. C. Schultz (1990). Learning sequential decision rules using simulation models and competition. Machine Learning 5(4),355–381.Google Scholar
  16. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.Google Scholar
  17. Koza, J. R. (1989). Hierarchical genetic algorithms operating on populations of computer programs. Proceedings of the 11th International Joint Conference on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann.Google Scholar
  18. Ramsey, C. L., A. C. Schultz and J. J. Grefenstette (1990). Simulation-assisted learning by competition: Effects of noise differences between training model and target environment. Proceedings of Seventh International Conference on Machine Learning (pp. 211–215). Austin, TX: Morgan Kaufmann.Google Scholar
  19. Schultz. A. C. (1991). Using a genetic algorithm to learn strategies for collision avoidance and local navigation. Proceedings of the Seventh International Symposium on Unmanned, Untethered Submersible Technology (pp. 213–225). Durham, NH.Google Scholar
  20. Schultz, A. C. and J. J. Grefenstette (1990). Improving tactical plans with genetic algorithms. Proceedings of IEEE Conference on Tools for AI 90 (pp. 328–334). Washington, DC: IEEE.Google Scholar
  21. Spears, W. M. and V. Anand (1991). A study of crossover operators in genetic programming. Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems (pp. 409–418). Charlotte, NC: Springer-Verlag.Google Scholar
  22. Spears, W. M. and K. A. De Jong (1990a). Using genetic algorithms for supervised concept learning. Proceedings of IEEE Conference on Tools for AI 90 (pp. 335–341). Washington, DC: IEEE.Google Scholar
  23. Spears, W. M. and K. A. De Jong (1990b). Using neural networks and genetic algorithms as heuristics for NP-complete problems. International Joint Conference on Neural Networks (pp. 118–121). Washington D.C: Lawrence Erlbaum Associates.Google Scholar
  24. Spears, W. M. and K. A. De Jong (1991a). An analysis of multi-point crossover. In Foundations of Genetic Algorithms, G. J. E. Rawlins (ed.), Bloomington, IN: Morgan Kaufmann.Google Scholar
  25. Spears, W. M. and K. A. De Jong (1991b). On the virtues of parameterized uniform crossover. Proceedings of the Fourth International Conference of Genetic Algorithms (pp. 230–236). San Diego, CA: Morgan Kaufmann.Google Scholar
  26. Spears, W. M. and D. F. Gordon (1991). Adaptive strategy selection for concept learning. Proceedings of the Workshop on Multistrategy Learning (pp. 231–246). Harpers Ferry, WV: George Mason University.Google Scholar
  27. Syswerda, G. (1989). Uniform crossover in genetic algorithms. Proceedings of the Third International Conference on Genetic Algorithms (pp. 2–9). Fairfax, VA: Morgan Kaufmann.Google Scholar
  28. Whitley, D., T. Starkweather and D. Fuquay (1989). Scheduling problems and traveling salesmen: The genetic edge recombination. Proceedings of the Third International Conference on Genetic Algorithms (pp. 133–141). Fairfax, VA: Morgan Kaufmann.Google Scholar

Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • John J. Grefenstette
    • 1
  • Kenneth A. De Jong
    • 1
  • William M. Spears
    • 1
  1. 1.Navy Center for Applied Research in Artificial Intelligence Information Technology DivisionNaval Research LaboratoryWashington, DC

Personalised recommendations