Using Expert Knowledge to Guide Covering and Mutation in a Michigan Style Learning Classifier System to Detect Epistasis and Heterogeneity

1. Urbanowicz, R., Moore, J.: LCSs: A Complete Introduction, Review, and Roadmap. Journal of Artificial Evolution and Applications 2009 (2009)

   Google Scholar 

2. Bacardit, J., Goldberg, D.E., Butz, M.V., Llorà, X., Garrell, J.M.: Speeding-Up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN VIII. LNCS, vol. 3242, pp. 1021–1031. Springer, Heidelberg (2004)

   Chapter  Google Scholar 

3. Bacardit, J., Stout, M., Hirst, J., Sastry, K., Llorà, X., Krasnogor, N.: Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 346–353. ACM (2007)

   Google Scholar 

4. Llorà, X., Sastry, K.: Fast rule matching for lcss via vector instructions. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1513–1520. ACM (2006)

   Google Scholar 

5. Bacardit, J., Burke, E., Krasnogor, N.: Improving the scalability of rule-based evolutionary learning. Memetic Computing 1(1), 55–67 (2009)

   Article  Google Scholar 

6. Franco, M., Krasnogor, N., Bacardit, J.: Speeding up the evaluation of evolutionary learning systems using gpgpus. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 1039–1046. ACM (2010)

   Google Scholar 

7. Shriner, D., Vaughan, L., Padilla, M., et al.: Problems with genome-wide association studies. Science 316(5833) (2007) 1840c

   Google Scholar 

8. Eichler, E., Flint, J., Gibson, G., Kong, A., Leal, S., Moore, J., Nadeau, J.: Missing heritability and strategies for finding the underlying causes of complex disease. Nature Reviews Genetics 11(6), 446–450 (2010)

   Article  Google Scholar 

9. Thornton-Wells, T., Moore, J., Haines, J.: Genetics, statistics and human disease: analytical retooling for complexity. TRENDS in Genetics 20(12), 640–647 (2004)

   Article  Google Scholar 

10. Urbanowicz, R., Moore, J.: The application of michigan-style lcss to address genetic heterogeneity and epistasis in association studies. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 195–202. ACM (2010)

    Google Scholar 

11. Urbanowicz, R., Granizo-Mackenzie, A., Moore, J.: An Analysis Pipeline with Visualization-Guided Knowledge Discovery for Michigan-Style LCSs. IEEE CIM Special Issue on Computational Intelligence in Bioinformatics (2012)

    Google Scholar 

12. Urbanowicz, R., Granizo-Mackenzie, A., Moore, J.: Instance-Linked Attribute Tracking and Feedback for Michigan-Style Supervised LCSs. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (2012)

    Google Scholar 

13. Jamshidi, M., et al.: Incorporating a-priori expert knowledge in genetic algorithms. In: Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 1997, pp. 300–305. IEEE (1997)

    Google Scholar 

14. Moore, J.H., White, B.C.: Exploiting Expert Knowledge in Genetic Programming for Genome-Wide Genetic Analysis. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN IX. LNCS, vol. 4193, pp. 969–977. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

15. Greene, C.S., White, B.C., Moore, J.H.: An Expert Knowledge-Guided Mutation Operator for Genome-Wide Genetic Analysis Using Genetic Programming. In: Rajapakse, J.C., Schmidt, B., Volkert, L.G. (eds.) PRIB 2007. LNCS (LNBI), vol. 4774, pp. 30–40. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

16. Greene, C.S., White, B.C., Moore, J.H.: Sensible initialization using expert knowledge for genome-wide analysis of epistasis using genetic programming. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 1289–1296. IEEE (2009)

    Google Scholar 

17. Greene, C.S., Penrod, N., Kiralis, J., Moore, J.: Spatially uniform relieff (surf) for computationally-efficient filtering of gene-gene interactions. BioData Mining 2(1), 1–9 (2009)

    Article  Google Scholar 

18. Bernadó-Mansilla, E., Garrell-Guiu, J.: Accuracy-based LCSs: models, analysis and applications to classification tasks. Evolutionary Computation 11(3), 209–238 (2003)

    Article  Google Scholar 

19. Wilson, S.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)

    Article  Google Scholar 

20. Orriols-Puig, A., Bernadó-Mansilla, E.: Revisiting ucs: Description, fitness sharing, and comparison with xcs. Learning Classifier Systems, 96–116 (2008)

    Google Scholar 

21. Moore, J.H., White, B.C.: Tuning ReliefF for Genome-Wide Genetic Analysis. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds.) EvoBIO 2007. LNCS, vol. 4447, pp. 166–175. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

22. Urbanowicz, R.J., Moore, J.H.: The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 404–413. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

23. Urbanowicz, R., Kiralis, J., Fisher, J., Moore, J.: Predicting Difficulty in Simulated Genetic Models: Metrics for Model Architecture Selection. BMC Bioinformatics (submitted)

    Google Scholar 

Download references