Skip to main content

Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA)

  • Chapter
Scalable Optimization via Probabilistic Modeling

Part of the book series: Studies in Computational Intelligence ((SCI,volume 33))

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Thierens, D., Goldberg, D.E.: Mixing in genetic algorithms. In Proceedings of the Fifth International Conference on Genetic Algorithms (1993) 38-45

    Google Scholar 

  2. Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms: Motivation, analysis and first results. Complex Systems 3 (1989) 493-530

    MATH  MathSciNet  Google Scholar 

  3. Harik, G.R.: Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms. PhD thesis, University of Michigan, Ann Arbor (1997) Also IlliGAL Report No. 97005

    Google Scholar 

  4. Kargupta, H.: SEARCH, polynomial complexity, and the fast messy genetic algorithm. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, IL (1995) Also IlliGAL Report No. 95008

    Google Scholar 

  5. Mühlenbein, H., Mahnig, T., Rodriguez, A.O.: Schemata, distributions and graphical models in evolutionary optimization. Journal of Heuristics 5(1999)215-247

    Article  MATH  Google Scholar 

  6. Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, PA (1994)

    Google Scholar 

  7. Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. In Proceedings of the International Conference on Evolutionary Compu-tation 1998 (ICEC ’98), IEEE New York (1998) 523-528

    Google Scholar 

  8. De Bonet, J.S., Isbell, C.L., Viola, P.: MIMIC: Finding optima by estimating probability densities. In Mozer, M.C., et al. (Eds.): Advances in Neural Information Processing Systems. Vol. 9, MIT, Cambridge (1997)424

    Google Scholar 

  9. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA (1989)

    MATH  Google Scholar 

  10. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI (1975)

    Google Scholar 

  11. Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm: I. continuous parameter optimization. Evolutionary Computation 1 (1993) 25-49

    Article  Google Scholar 

  12. Syswerda, G.: Uniform crossover in genetic algorithms. In Schaffer, J.D., (Ed.): Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufman, San Mateo, CA (1989) 2-9

    Google Scholar 

  13. Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, New York (1991)

    Book  MATH  Google Scholar 

  14. Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)

    MATH  Google Scholar 

  15. Lobo, F.G., Harik, G.R.: Extended compact genetic algorithm in C++. IlliGAL Report No. 99016, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL (1999)

    Google Scholar 

  16. Sastry, K.: Evaluation-relaxation schemes for genetic and evolutionary algorithms. Master’s thesis, University of Illinois at Urbana-Champaign, Urbana, IL (2001) Also IlliGAL Report No. 2002004

    Google Scholar 

  17. Sastry, K., Goldberg, D.E.: Designing competent mutation operator via probabilistic model building of neighborhoods. In Deb, K.et al., (Eds.): Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2004, Springer, Berlin Heidelberg New York (2004) 114-125 Part II, LNCS 3103

    Google Scholar 

  18. Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: Bayesian optimization algorithm, population sizing, and time to convergence. In Whitley, D.et al., (Eds.): Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2000, Morgan Kaufmann, San Francisco, CA (2000) 275-282

    Google Scholar 

  19. Pelikan, M., Sastry, K., Goldberg, D.E.: Scalability of the Bayesian optimization algorithm. International Journal of Approximate Reasoning 31(2003)221-258

    Article  MathSciNet  Google Scholar 

  20. Lima, C.F., Sastry, K., Goldberg, D.E., Lobo, F.G.: Combining competent crossover and mutation operators: A probabilistic model building approach. In Beyer, H.G.et al., (Eds.): Proceedings of the ACM SIGEVO Genetic and Evolutionary Computation Conference GECCO-2005, ACM, NY, USA (2005) 735-742

    Chapter  Google Scholar 

  21. Harik, G.R., Lobo, F.G.: A parameter-less genetic algorithm. In Banzhaf, W., et al. (Eds.): Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99, Morgan Kaufmann, San Francisco, CA (1999)258-265

    Google Scholar 

  22. Lobo, F.G.: The parameter-less genetic algorithm: Rational and automated parameter selection for simplified genetic algorithm operation. PhD thesis, Universidade Nova de Lisboa, Portugal (2000) Also IlliGAL Report No. 2000030

    Google Scholar 

  23. Harik, G.R.: Linkage learning via probabilistic modeling in the ECGA. IlliGAL Report No. 99010, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbadna-Champaign, Urbana, IL (1999)

    Google Scholar 

  24. Ducheyne, E.I., De Wulf, R.R., De Baets, B.: Using linkage learning fo forest management planning. In Cantú-Paz, E., (Ed.): Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO2002), AAAI, New York (2002) 109-114

    Google Scholar 

  25. Sastry, K.: Efficient cluster optimization using extended compact genetic algorithm with seeded population. In Workshop Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco, CA, USA (2001) 222-225

    Google Scholar 

  26. Lipinski, P.: Evolutionaty data-mining methods in discovering stock market expertise from financial time series. PhD thesis, Université Louis Pasteur and University of Wroclaw, Strasbourg and Wroclaw (2004)

    Google Scholar 

  27. Butz, M.V., Pelikan, M., Llora, X., Goldberg, D.E.: Automated global structure extraction for effective local building block processing in XCS. IlliGAL Report No. 2005011, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL (2005)

    Google Scholar 

  28. Lobo, F.G., Lima, C.F., Mártires, H.: Massive parallelization of the compact genetic algorithm. In Ribeiro, B.et al., (Eds.): Adaptive and Natural Computing Algorithms. Springer Computer Series, Springer, Berlin Heidelberg New York (2005) 530-533

    Chapter  Google Scholar 

  29. Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: The Bayesian Optimization Algorithm. In Banzhaf, W.et al., (Eds.): Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99, Morgan Kaufmann, San Francisco, CA (1999) 525-532

    Google Scholar 

  30. Pelikan, M.: Hierarchical Bayesian optimization algorithm: Toward a new generation of evolutionary algorithms. Springer, Berlin Heidelberg New York (2005)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Harik, G.R., Lobo, F.G., Sastry, K. (2006). Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA). In: Pelikan, M., Sastry, K., CantúPaz, E. (eds) Scalable Optimization via Probabilistic Modeling. Studies in Computational Intelligence, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-34954-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-34954-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34953-2

  • Online ISBN: 978-3-540-34954-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics