Information distance based fitness and diversity metrics

Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication

Volumn , Issue , 2010, Pages 1851-1854

  Card, Stuart W ^a  

^a SYRACUSE UNIVERSITY   (United States)

Author keywords

Algorithmic information; Complexity; Distance; Diversity; Entropy; Evolutionary computation; Fitness; Interaction information; Machine learning; Metric; Mutual information

Indexed keywords

ALGORITHMIC INFORMATION; COMPLEXITY; DISTANCE; DIVERSITY; EVOLUTIONARY COMPUTATIONS; FITNESS; INTERACTION INFORMATION; MACHINE-LEARNING; METRIC; MUTUAL INFORMATIONS;

CALCULATIONS; COMPUTATIONAL COMPLEXITY; ENTROPY; EVOLUTIONARY ALGORITHMS; HEALTH; HUMAN COMPUTER INTERACTION; INFORMATION THEORY; LEARNING SYSTEMS; PARALLEL PROCESSING SYSTEMS; POPULATION STATISTICS; PROCESS MONITORING; REAL VARIABLES; TECHNICAL PRESENTATIONS;

MATHEMATICAL MODELS;

EID: 77955955287     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1830761.1830815     Document Type: Conference Paper

References (17)

1. Altenberg, L. 1995. The Schema Theorem and Price's Theorem. In L.D. Whitley and M.D. Vose, editors, Foundations of Genetic Algorithms 3, pages 23-49. Morgan Kaufmann, San Francisco, CA.
2. Card, S. and Mohan, C. 2005. Information theoretic indicators of fitness, relevant diversity & pairing potential in genetic programming. In Proceedings of the 2005 IEEE Congress on Evolutionary Computation, volume 3, pages 2545-2552. IEEE Press, Edinburgh, UK.
3. Shannon, C. 1948. A Mathematical Theory of Communication. In The Bell System Technical Journal, Vol. 27, pages 379-423 and 623-656.
4. Koza, J., Al-Sakran, S. and Jones, L. 2005. Cross-Domain Features of Runs of Genetic Programming Used to Evolve Designs for Analog Circuits, Optical Lens Systems, Controllers, Antennas, Mechanical Systems, and Quantum Computing Circuits. In Proc. of the 2005 NASA/DOD Conference on Evolvable Hardware.
5. Daida, J. 2005. Towards Identifying Populations that Increase the Likelihood of Success in Genetic Programming. In Proc. of the 2005 Genetic and Evolutionary Computation Conference (GECCO '05).
6. McGill, W.J. 1954. Multivariate information transmission. In Psychometrika 19, pages 97-116.
7. Chechik, G. 2003. An Information Theoretic Approach to the Study of Auditory Coding. PhD Thesis, Hebrew University.
8. Li, M. and Vitanyi, P. 2008. An Introduction to Kolmogorov Complexity and its Applications. Springer-Verlag, New York, NY.
9. Grunwald, P. and Vitanyi, P. 2004. Shannon Information and Kolmogorov Complexity. http://arxiv.org/abs/cs/0410002
10. Deza, M. and Deza, E. 2009. Encyclopedia of Distances. Springer-Verlag, Heidelberg.
11. Card, S. and Mohan, C. 2007. Towards an information theoretic framework for genetic programming. In R. Riolo, T. Soule and W. Worzel, editors, Genetic Programming Theory & Practice V, pages 87-106. Springer, New York, NY.
12. Kraskov, A. and Grassberger, P., 2008. MIC: Mutual Information Based Hierarchical Clustering. In Information Theory and Statistical Learning, pages 101-123. Springer, New York, NY.
13. Li, M., Chen, X., Li, X., Ma, B. and Vitanyi, P., 2004. The similarity metric. In IEEE Trans. On Information Theory 50 (12), pages 3250-3264.
14. Shalizi, C. 2003. Complexity, Entropy and the Physics of gzip. http://www.cscs.umich.edu/~crshalizi/notabene/cep-gzip.html
15. Card, S. and Mohan, C. 2008. An application of information theoretic selection to evolution of models with continuous valued inputs. In Genetic Programming Theory & Practice VI, pages 29-42. Springer, New York, NY.
16. Rissanen, J. 2007. Information and Complexity in Statistical Modeling. Springer, New York, NY.
17. Ching, J., Wong, A. and Chan, K., 1995. Class-dependent discretization for inductive learning from continuous and mixed-mode data. In IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 17, no. 7, pages 641-651.