Warianty tytułu
Based on entropy population diversity in genetic programming with graph representation
Języki publikacji
Abstrakty
Artykuł przedstawia wyniki badań różnorodności populacji w programowaniu genetycznym z reprezentacją grafową. Celem głębszego zrozumienia warunków pomyślnej ewolucji algorytmów programowania genetycznego zbadano kilka miar różnorodności związanych z pojęciem entropii. Analizę korelacji miar z jakością wyników przeprowadzono za pomocą standardowych statystyk.
This paper presents a study of population diversity in genetic programming with graph representation. Several measures of diversity based on entropy are considered to gain a deeper understanding of the conditions under which the evolution of genetic programming algorithms is successful. Standard statistics are used to analyze their correlation with performance.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
51-69
Opis fizyczny
Bibliogr. 23 poz.
Twórcy
autor
- ABG S.A. 02-017 Warszawa Al. Jerozolimskie 123 A tel. 022 60 77 200, biuro@abg.com.pl
Bibliografia
- 1. Koza J. R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. Complex Adaptive Systems, MIT Press, Cambridge, MA, 1992.
- 2. Koza J. R.: Genetic Programming U: Automatic Discovery of Reusable Programs. Complex Adaptive Systems, MIT Press, Cambridge. MA, 1994.
- 3. Koza J. R., Bennett HI F. H., Andre D., Keane M. A.: Genetic Programming III: Automatic Darvinian Invention and Problem Solving. Morgan Kaufmann, San Francisco 1999.
- 4. Shannon C. E: A mathematical theory of communication. Bell System Technical Journal 27 1948, s. 379-423, 623-653.
- 5. Cover T. M., Thomas J. A.: Elements of Information Theory, Wiley, New York 1991, s. 16-24.
- 6. Dawkins R.: Fenotyp rozszerzony. Prószyński i S-ka SA, Warszawa 2003, s. 38-39, 62-63.
- 7. Miller, J. F.: An Empirical Study of the Efficiency of Learning Boolean Functions using a Cartesian Genetic Programming Approach, GECCO 1999.
- 8. Langdon W. B., Poli, R.: Foundations of Genetic Programming. Springer-Verlag, 2002.
- 9. McPhee N. F., Hopper N.J.: Analysis of genetic diversity trough population history. In W. Banzhaf et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, Florida, USA, 1999, s. 1112-1120.
- 10. Keijzer M.: Efficiently representing populations in genetic programming. In P. J. Angelina and K. E. Kinnear, Jr., editors, Advances in Genetic Programming 2, chapter 13, MIT Press, Cambridge, MA, USA, 1996, s. 259-278.
- 11. Langdon W. B.: Data Structures and Genetic Programming: Genetic Programming Data Structures - Automatic Programming!. Vol. 1 of Genetic Programming. Kluwer, Boston 1998.
- 12. Burke E., Gustafson S., Kendall G.: Genetic Programming Search Behaviour: Viewing Population Dynamics with Diversity Measures. Computer Science Technical Report No. NOTTCS-TR-2003-1, 2003.
- 13. Ryan C: Pygmies and civil servants. In K.E. Kinnear, Jr., editor, Advances in Genetic Programming, MIT, 1994, s. 243-263.
- 14. Ekart A., Nemeth S.: A metric for genetic programs and fitness sharing. In R. Poli et al., editors, Proceedings of the European Conference on Genetic Programming, volume 1802 of LNCS, Edinburgh, 2000. Springer-Verlag, s. 259-270.
- 15. Lukę S., ECJ: A Java-based evolutionary computation and genetic programming system http://www.cs.umd.edu/projects/plus/ecj/.
- 16. Miller J. F., Thomson P.: Cartesian Genetic Programming. Red. R. Poli, W. Banzhaf, W. B. Langdon, J. F. Miller, P. Nordin. T. C. Fogaity. Proceedings of the Third European Conference on Genetic Programming (EuroGP2000). LNCS, Vol. 1802, Springer-Verlag , Heidelberg 2000, s. 121-132.
- 17. Poli. R: Evolution of Graph-like Programs with Parallel Distributed Genetic Programming. Red. T. Back. Genetic Algorithms: Proceedings of the Seventh International Conference. s. 346-353. Michigan State University. East Dansing. MI. USA. Morgan Kaufmann, 1997.
- 18. Rosca J. P.: Entropy-driven adaptive representation. Red. J. P. Rosca, Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, Tahoe City. California, USA, 1995.
- 19. Shalizi R.. Camperi M. F., Klinkner K. L.: Discovering functional communities in dynamical networks. ICML, 2006.
- 20. Teller A., Veloso M.: Algorithm evolution for face recognition: Wliat makes a picture difficult. Proceedings of the International Conference on Evolutionary Compntation. EEEE Press, 1995.
- 21. Teller A., Veloso M.: Program Evolution for Data Miting. The International Journal of Expert Systems, 1995, s. 213-236.
- 22. Maes, F., Collignon, A, Vandermeulen, D., Marchal. G., Suetens. P: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging. 1997.
- 23. Koronacki J.. Mielniczuk J.: Statystyka dla studentów kierunków technicznych i przyrodniczych. WNT, Warszawa 2006
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BSL3-0017-0047