Have a personal or library account? Click to login
Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming Cover

Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming

Open Access
|Dec 2017

References

  1. [1] Bell R., Koren Y., and Volinsky C. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 95-104. ACM, 2007.10.1145/1281192.1281206
  2. [2] Bongard J. and Lipson H. Active coevolutionary learning of deterministic finite automata. Journal of Machine Learning Research, 6(Oct):1651-1678, 2005.
  3. [3] Breese J. S., Heckerman D., and Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pages 43-52. Morgan Kaufmann Publishers Inc., 1998.
  4. [4] Chellapilla K. and Fogel D. B. Evolving an expert checkers playing program without using human expertise. IEEE Transactions on Evolutionary Computation, 5(4):422-428, 2001.10.1109/4235.942536
  5. [5] Chong S. Y., Tino P., Ku D. C., and Xin Y. Improving Generalization Performance in Co-Evolutionary Learning. IEEE Transactions on Evolutionary Computation, 16(1):70-85, 2012.10.1109/TEVC.2010.2051673
  6. [6] Clark D. M. Evolution of algebraic terms 1: term to term operation continuity. International Journal of Algebra and Computation, 23(05):1175-1205, 2013.
  7. [7] Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182-197, April 2002.10.1109/4235.996017
  8. [8] Ficici S. G. and Pollack J. B. Pareto optimality in coevolutionary learning. In Kelemen J. and Sosík P., editors, Advances in Artificial Life, 6th European Conference, ECAL 2001, volume 2159 of Lecture Notes in Computer Science, pages 316-325, Prague, Czech Republic, 2001. Springer.10.1007/3-540-44811-X_34
  9. [9] Fortin F.-A., De Rainville F.-M., Gardner M.-A., Parizeau M., and Gagné C. DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13:2171-2175, jul 2012.
  10. [10] Gonçalves I., Silva S., Melo J. B., and Carreiras J. M. Random sampling technique for overfitting control in genetic programming. In Genetic Programming, pages 218-229. Springer, 2012.10.1007/978-3-642-29139-5_19
  11. [11] Helmuth T., Spector L., and Matheson J. Solving uncompromising problems with lexicase selection. IEEE Transactions on Evolutionary Computation, 19(5):630-643, Oct. 2015.10.1109/TEVC.2014.2362729
  12. [12] Hofmann T. Collaborative filtering via gaussian probabilistic latent semantic analysis. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 259-266. ACM, 2003.10.1145/860435.860483
  13. [13] Hollander M., Wolfe D. A., and Chicken E. Nonparametric statistical methods, volume 751. John Wiley & Sons, 2013.
  14. [14] Jin Y., Olhofer M., and Sendhoff B. A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation, 6:481-494, 2002.10.1109/TEVC.2002.800884
  15. [15] Kanji G. K. 100 statistical tests. Sage, 2006.10.4135/9781849208499
  16. [16] Koren Y., Bell R., and Volinsky C. Matrix factorization techniques for recommender systems. Computer, (8):30-37, 2009.10.1109/MC.2009.263
  17. [17] Krawiec K. and Lichocki P. Using co-solvability to model and exploit synergetic effects in evolution. In Schaefer R., Cotta C., Kolodziej J., and Rudolph G., editors, PPSN 2010 11th International Conference on Parallel Problem Solving From Nature, volume 6239 of Lecture Notes in Computer Science, pages 492-501, Krakow, Poland, 11-15 Sept. 2010. Springer.10.1007/978-3-642-15871-1_50
  18. [18] Krawiec K. and Liskowski P. Automatic derivation of search objectives for testbased genetic programming. In Machado P., Heywood M. I., McDermott J., Castelli M., Garcia-Sanchez P., Burelli P., Risi S., and Sim K., editors, 18th European Conference on Genetic Programming, volume 9025 of LNCS, pages 53-65, Copenhagen, 8-10 Apr. 2015. Springer.10.1007/978-3-319-16501-1_5
  19. [19] Lee D. D. and Seung H. S. Algorithms for non-negative matrix factorization. In Advances in neural information processing systems, pages 556-562, 2001.
  20. [20] Liskowski P. and Jaskowski W. Accelerating coevolution with adaptive matrix factorization. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’17, pages 457-464, New York, NY, USA, 2017. ACM.10.1145/3071178.3071320
  21. [21] Liskowski P. and Krawiec K. Discovery of implicit objectives by compression of interaction matrix in test-based problems. In Parallel Problem Solving from Nature-PPSN XIII, pages 611-620. Springer, 2014.10.1007/978-3-319-10762-2_60
  22. [22] Liskowski P. and Krawiec K. Non-negative matrix factorization for unsupervised derivation of search objectives in genetic programming. In Friedrich T., editor, GECCO ’16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation, pages 749-756, Denver, USA, 20-24 July 2016. ACM.10.1145/2908812.2908888
  23. [23] Liskowski P. and Krawiec K. Surrogate fitness via factorization of interaction matrix. In Heywood M. I., McDermott J., Castelli M., Costa E., and Sim K., editors, EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming, volume 9594 of LNCS, pages 68-82, Porto, Portugal, 30 Mar.-1 Apr. 2016. Springer Verlag.10.1007/978-3-319-30668-1_5
  24. [24] Liskowski P. and Krawiec K. Discovery of search objectives in continuous domains. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’17, pages 969-976, New York, NY, USA, 2017. ACM.10.1145/3071178.3071344
  25. [25] Liskowski P. and Krawiec K. Online discovery of search objectives for test-based problems. Evolutionary Computation, 25(3):375-406, 2017.10.1162/evco_a_00179
  26. [26] Liskowski P., Krawiec K., Helmuth T., and Spector L. Comparison of semanticaware selection methods in genetic programming. In Johnson C., Krawiec K., Moraglio A., and O’Neill M., editors, GECCO 2015 Semantic Methods in Genetic Programming (SMGP’15) Workshop, pages 1301-1307, Madrid, Spain, 11-15 July 2015. ACM.10.1145/2739482.2768505
  27. [27] McKay R. I. B. Fitness sharing in genetic programming. In Whitley D., Goldberg D., Cantu-Paz E., Spector L., Parmee I., and Beyer H.-G., editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 435-442, Las Vegas, Nevada, USA, 10-12 July 2000. Morgan Kaufmann.
  28. [28] Pagie L. and Hogeweg P. Evolutionary consequences of coevolving targets. Evolutionary Computation, 5(4):401-418, Winter 1997.10.1162/evco.1997.5.4.40110021765
  29. [29] Ricci F., Rokach L., and Shapira B. Introduction to recommender systems handbook. Springer, 2011.10.1007/978-0-387-85820-3
  30. [30] Schmidt M. D. and Lipson H. Coevolution of fitness predictors. IEEE Transactions on Evolutionary Computation, 12(6):736-749, Dec. 2008.10.1109/TEVC.2008.919006
  31. [31] Smith R. E., Forrest S., and Perelson A. S. Searching for diverse, cooperative populations with genetic algorithms. Evolutionary computation, 1(2):127-149, 1993.10.1162/evco.1993.1.2.127
  32. [32] Spector L., Clark D. M., Lindsay I., Barr B., and Klein J. Genetic programming for finite algebras. In Keijzer M., editor, GECCO ’08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 1291-1298, Atlanta, GA, USA, 12-16 July 2008. ACM.10.1145/1389095.1389343
  33. [33] Stanley K. O. and Miikkulainen R. Competitive coevolution through evolutionary complexification. J. Artif. Intell. Res.(JAIR), 21:63-100, 2004.10.1613/jair.1338
DOI: https://doi.org/10.1515/fcds-2017-0017 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
Language: English
Page range: 339 - 358
Submitted on: Apr 12, 2017
Accepted on: Aug 23, 2017
Published on: Dec 9, 2017
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2017 Krzysztof Krawiec, Paweł Liskowski, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.