Difficulty of Unimodal and Multimodal Landscapes in Genetic Programming

1. K. Chellapilla. Evolutionary programming with tree mutations: Evolving computer programs without crossover. In J.R. Koza, K. Deb, M. Dorigo, D.B. Fogel, M. Garzon, H. Iba, and R.L. Riolo, editors, Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 431–438, Stanford University, CA, USA, 1997. Morgan Kaufmann.

   Google Scholar 

2. M. Clergue, P. Collard, M. Tomassini, and L. Vanneschi. Fitness distance correlation and problem difficulty for genetic programming. In Proceedings of the genetic and evolutionary computation conference GECCO’02, pages 724–732. Morgan Kaufmann, San Francisco, CA, 2002.

   Google Scholar 

3. P. Collard, A. Gaspar, M. Clergue, and C. Escazut. Fitness distance correlation as statistical measure of genetic algorithms difficulty, revisited. In European Conference on Artificial Intelligence (ECAI’98), pages 650–654, Brighton, 1998. John Witley & Sons, Ltd.

   Google Scholar 

4. E.D. de Jong, R.A. Watson, and J.B. Pollak. Reducing bloat and promoting diversity using multi-objective methods. In L. Spector et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference GECCO’ 01, San Francisco, CA, July 2001. Morgan Kaufmann.

   Google Scholar 

5. K. Deb and D.E. Goldberg. Analyzing deception in trap functions. In D. Whitley, editor, Foundations of Genetic Algorithms, 2, pages 93–108. Morgan Kaufmann, 1993.

   Google Scholar 

6. K. Deb, J. Horn, and D. Goldberg. Multimodal deceptive functions. Complex Systems, 7:131–153, 1993.

   MATH  Google Scholar 

7. A. Ekárt and S.Z. Németh. Maintaining the diversity of genetic programs. In J.A. Foster, E. Lutton, J. Miller, C. Ryan, and A.G.B. Tettamanzi, editors, Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002, volume 2278 of LNCS, pages 162–171, Kinsale, Ireland, 3–5 April 2002. Springer-Verlag.

   Google Scholar 

8. C. Gathercole and P. Ross. An adverse interaction between crossover and restricted tree depth in genetic programming. In J.R. Koza, D.E. Goldberg, D.B. Fogel, and R.L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 291–296, Stanford University, CA, USA, 28–31 July 1996. MIT Press.

   Google Scholar 

9. T. Jones. Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, University of New Mexico, Albuquerque, 1995.

   Google Scholar 

10. W.B. Langdon and R. Poli. An analysis of the max problem in genetic programming. In J.R. Koza, K. Deb, M. Dorigo, D.B. Fogel, M. Garzon, H. Iba, and R.L. Riolo, editors, Genetic Programming 1997: Proceedings of the Second Annual Conference on Genetic Programming, pages 222–230, San Francisco, CA, 1997. Morgan Kaufmann.

    Google Scholar 

11. N.F. McPhee and N.J. Hopper. Analysis of genetic diversity through population history. In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela, and R. E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 1112–1120, Orlando, Florida, USA, 13–17 July 1999. Morgan Kaufmann.

    Google Scholar 

12. U.-M. O’Reilly. An Analysis of Genetic Programming. PhD thesis, Carleton University, Ottawa, Ontario, Canada, 1995.

    Google Scholar 

13. U.-M. O’Reilly. Using a distance metric on genetic programs to understand genetic operators. In Proceedings of 1997 IEEE International Conference on Systems, Man, and Cybernetics, pages 4092–4097. IEEE Press, Piscataway, NJ, 1997.

    Google Scholar 

14. B. Punch, D. Zongker, and E. Goodman. The royal tree problem, a benchmark for single and multiple population genetic programming. In P. Angeline and K. Kinnear, editors, Advances in Genetic Programming 2, pages 299–316, Cambridge, MA, 1996. The MIT Press.

    Google Scholar 

15. R.J. Quick, V.J. Rayward-Smith, and G.D. Smith. Fitness distance correlation and ridge functions. In Fifth Conference on Parallel Problems Solving from Nature (PPSN’98), pages 77–86. Springer-Verlag, Heidelberg, 1998.

    Chapter  Google Scholar 

16. V. Slavov and N.I. Nikolaev. Fitness landscapes and inductive genetic programming. In Proceedings of International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA97), University of East Anglia, Norwich, UK, 1997. Springer-Verlag KG, Vienna.

    Google Scholar 

17. L. Vanneschi, M. Tomassini, P. Collard, and M. Clergue. Fitness distance correlation in structural mutation genetic programming. In C. Ryan et al., editor, Genetic Programming, 6th European Conference, EuroGP2003, Lecture Notes in Computer Science. Springer-Verlag, Heidelberg, 2003.

    Google Scholar 

Download references