Adaptive sampling for active learning with genetic programming

Atlas, L. E., Cohn, D., & Ladner, R. (1990) Training connectionist networks with queries and selective sampling. In Advances in neural information processing systems (Vol. 2, pp 566–573). Morgan-Kaufmann. Balkanski, E. & Singer, Y. (2018a). The adaptive complexity of maximizing a submodular function. In: I. Diakonikolas, D. Kempe, M. Henzinger (eds.), Proceedings of the 50th annual ACM SIGACT symposium on theory of computing, STOC 2018, Los Angeles, CA, USA, June 25–29, 2018 (pp 1138–1151). ACM, doi:10.1145/3188745.3188752. Balkanski, E. & Singer, Y. (2018b). Approximation guarantees for adaptive sampling. In: J.G. Dy, A. Krause (eds.) Proceedings of the 35th international conference on machine learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10–15, 2018, PMLR, Proceedings of Machine Learning Research (vol 80, pp 393–402). http://proceedings.mlr.press/v80/balkanski18a.html. CGP. (2009). Cartesian gp website. http://www.cartesiangp.co.uk. Cohn, 1994, Improving generalization with active learning, Machine Learning, 15, 201, 10.1007/BF00993277 Curry, R. & Heywood, M. I. (2004). Towards efficient training on large datasets for genetic programming. In Advances in artificial intelligence, 17th conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2004, Proc., Springer, lecture notes in computer science. (vol 3060, pp 161–174), doi:10.1007/978-3-540-24840-8_12. Deschrijver, 2011, Adaptive sampling algorithm for macromodeling of parameterized s -parameter responses, IEEE Transactions on Microwave Theory and Techniques, 59, 39, 10.1109/TMTT.2010.2090407 Eiben, 2007, Parameter control in evolutionary algorithms, 19 Freitas, 2002 Fu, 2013, A survey on instance selection for active learning, Knowledge and Information Systems, 35, 249, 10.1007/s10115-012-0507-8 Gathercole, C. (1998). An investigation of supervised learning in genetic programming. Thesis, University of Edinburgh Gathercole, 1994, Dynamic training subset selection for supervised learning in genetic programming, Vol. 866, 312 Ghatercole, 1997, Small populations over many generations can beat large populations over few generations in genetic programming, 111 Gonçalves, I. & Silva, S. (2013). Balancing learning and overfitting in genetic programming with interleaved sampling of training data. In K. Krawiec, A. Moraglio, T. Hu, A.S. Etaner-Uyar, B. Hu (eds.), Genetic programming – 16th European conference, EuroGP 2013, Vienna, Austria, April 3–5, 2013. Proceedings, Springer, lecture notes in computer science (Vol. 7831, pp 73–84), doi:10.1007/978-3-642-37207-0_7. Haitao, 2018, A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design, Structural and Multidisciplinary Optimization, 57 Harding, 2011, Implementing cartesian genetic programming classifiers on graphics processing units using gpu.net, 463 Hmida, H., Ben Hamida, S., Borgi, A., & Rukoz, M. (2016a). Hierarchical data topology based selection for large scale learning. In 2016 Intl IEEE conferences on ubiquitous intelligence & computing, advanced and trusted computing, scalable computing and communications, cloud and big data computing, internet of people, and smart world congress, Toulouse, France, July 18–21, 2016 (pp 1221–1226), IEEE, doi:10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0186, URL http://doi.ieeecomputersociety.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0186 Hmida, H., Ben Hamida, S., Borgi, A., & Rukoz, M. (2016b). Sampling methods in genetic programming learners from large datasets: A comparative study. In P. Angelov, Y. Manolopoulos, L.S. Iliadis, A. Roy, M.M.B.R. Vellasco (eds.), Advances in big data – proceedings of the 2nd INNS conference on big data, October 23–25, 2016, Thessaloniki, Greece, advances in intelligent systems and computing (vol 529, pp 50–60), doi:10.1007/978-3-319-47898-2_6. Hunt, R., Johnston, M., Browne, W. N., & Zhang, M. (2010). Sampling methods in genetic programming for classification with unbalanced data. In AI 2010: Advances in artificial intelligence – 23rd Australasian joint conference, proc., Springer, lecture notes in computer science (Vol. 6464, pp 273–282), doi:10.1007/978-3-642-17432-2_28. Iba, H. (1999). Bagging, boosting, and bloating in genetic programming. In The 1st annual conference on genetic and evolutionary computation, Proc., Morgan Kaufmann, San Francisco, CA, USA, GECCO’99, vol 2, pp 1053–1060 Iyengar, V. S., Apté, C., & Zhang, T. (2000). Active learning using adaptive resampling. In R. Ramakrishnan, S.J. Stolfo, R.J. Bayardo, I. Parsa (eds.), Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, Boston, MA, USA, August 20–23, 2000 (pp 91–98). ACM, doi:10.1145/347090.347110. Kohavi, 1996, Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid, 202 Koza, 1992 Lasarczyk, 2004, Dynamic subset selection based on a fitness case topology, Evolutionary Computation, 12, 223, 10.1162/106365604773955157 Li, 2002, Data reduction via adaptive sampling, Communications in Information and Systems, 2, 53, 10.4310/CIS.2002.v2.n1.a3 Li, 2008, Adaptive data reduction for large-scale transaction data, European Journal of Operational Research, 188, 910, 10.1016/j.ejor.2007.08.008 Liu, H., Xu, S., Ma, Y., Chen, X., & Wang, X. (2015) An adaptive bayesian sequential sampling approach for global metamodeling. Journal of Mechanical Design 138(1), doi:10.1115/1.4031905, 011404, https://asmedigitalcollection.asme.org/mechanicaldesign/article-pdf/138/1/011404/6227283/md_138_01_011404.pdf. Liu, 2004, Reducing overfitting in genetic programming models for software quality classification, 56 Luke, S. (2017). Ecj homepage. http://cs.gmu.edu/~eclab/projects/ecj/. Luo, 2017, Sampling-based adaptive bounding evolutionary algorithm for continuous optimization problems, Information Sciences, 382–383, 216, 10.1016/j.ins.2016.12.023 Miller, J. F. & Thomson, P. (2000). Cartesian genetic programming. In Genetic programming, European conference, proc., Springer, lecture notes in computer science (Vol. 1802, pp 121–132). Nordin, 1997, An on-line method to evolve behavior and to control a miniature robot in real time with genetic programming, Adaptive Behaviour, 5, 107, 10.1177/105971239700500201 Paris, G., Robilliard, D., Fonlupt, C. (2003). Exploring overfitting in genetic programming. In: P. Liardet, P. Collet, C. Fonlupt, E. Lutton, M. Schoenauer (eds.), Artificial evolution, 6th international conference, evolution Artificielle, EA 2003, Marseilles, France, October 27–30, 2003, Springer, lecture notes in computer science (Vol. 2936, pp 267–277), doi:10.1007/978-3-540-24621-3_22. Pétrowski, A. & Ben Hamida, S. (2017). Evolutionary algorithms. John Wiley & Sons, USA, doi:10.1002/9781119136378 Pickett, B. & Turner, C. J. (2011). A review and evaluation of existing adaptive sampling criteria and methods for the creation of NURBs-based Metamodels. In 31st Computers and information in engineering conference. (Vol. 2, Parts A and B, pp 609–618), doi:10.1115/DETC2011-47288. Settles, B. (2010). Active learning literature survey. Tech. Rep. 1648, University of Wisconsin, Madison. Simon, D. (2013). Evolutionary optimization algorithms. John Wiley & Sons, USA, doi:10.1007/978-1-84996-129-5 UCI. (1999). Kdd cup. https://archive.ics.uci.edu/ml/datasets/KDD+Cup+1999+Data. Yu, 2010, Introduction to evolutionary algorithms, Decision Engineering, Springer, London, London, Zhang, 1999, Genetic programming with active data selection, 146 Zhang, 1999, Genetic programming with incremental data inheritance, Vol. 2, 1217