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Genetic Programming Theory and Practice XV pp 121–134Cite as

A System for Accessible Artificial Intelligence

Part of the Genetic and Evolutionary Computation book series (GEVO)

Abstract

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.

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Notes

  1. 1.

    FGLab: https://github.com/Kaixhin/FGLab.

  2. 2.

    http://lacava.github.io/few.

  3. 3.

    http://epistasislab.github.io/ellyn.

  4. 4.

    https://github.com/rhiever/tpot.

References

  1. Arnaldo, I., Veeramachaneni, K., Song, A., O’Reilly, U.M.: Bring your own learner: A cloud-based, data-parallel commons for machine learning. IEEE Computational Intelligence Magazine 10(1), 20–32 (2015)

    CrossRef  Google Scholar 

  2. Bruce, G., Buchanan, B., Shortliffe, E.: Rule-based expert systems: The MYCIN experiments of the Stanford heuristic programming project (1984)

    Google Scholar 

  3. Chodorow, K., Dirolf, M.: MongoDB: The Definitive Guide, 1st edn. O’Reilly Media, Inc. (2010)

    Google Scholar 

  4. Demšar, J., Curk, T., Erjavec, A., Črt Gorup, Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., Zupan, B.: Orange: Data mining toolbox in Python. Journal of Machine Learning Research 14, 2349–2353 (2013)

    Google Scholar 

  5. Ferrucci, D.A.: Introduction to “This is Watson”. IBM Journal of Research and Development 56(3.4), 1–1 (2012)

    CrossRef  Google Scholar 

  6. Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT Press (2016)

    Google Scholar 

  7. Hastie, T.J., Tibshirani, R.J., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York, NY, USA (2009)

    Google Scholar 

  8. Kalousis, A.: Algorithm selection via meta-learning. Ph.D. thesis, Universite de Geneve (2002)

    Google Scholar 

  9. Kannappan, K., Spector, L., Sipper, M., Helmuth, T., La Cava, W., Wisdom, J., Bernstein, O.: Analyzing a decade of human-competitive (“HUMIE”) winners: What can we learn? In: Genetic Programming Theory and Practice XII, pp. 149–166. Springer International Publishing (2015)

    Google Scholar 

  10. Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection, vol. 1. MIT Press (1992)

    Google Scholar 

  11. La Cava, W., Danai, K., Spector, L.: Inference of compact nonlinear dynamic models by epigenetic local search. Engineering Applications of Artificial Intelligence 55, 292–306 (2016)

    CrossRef  Google Scholar 

  12. La Cava, W., Danai, K., Spector, L., Fleming, P., Wright, A., Lackner, M.: Automatic identification of wind turbine models using evolutionary multiobjective optimization. Renewable Energy 87, 892–902 (2016)

    CrossRef  Google Scholar 

  13. La Cava, W., Moore, J.: A general feature engineering wrapper for machine learning using 𝜖-lexicase survival. In: European Conference on Genetic Programming, pp. 80–95. Springer (2017)

    Google Scholar 

  14. La Cava, W., Moore, J.H.: Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods. In: GECCO ‘17: Proceedings of the Conference on Genetic and Evolutionary Computation. ACM (2017)

    Google Scholar 

  15. La Cava, W., Silva, S., Vanneschi, L., Spector, L., Moore, J.: Genetic programming representations for multi-dimensional feature learning in biomedical classification. In: European Conference on the Applications of Evolutionary Computation, pp. 158–173. Springer (2017)

    Google Scholar 

  16. Langley, P.: Lessons for the Computational Discovery of Scientific Knowledge (2002)

    Google Scholar 

  17. Moore, J.H., Andrews, P.C., Barney, N., White, B.C.: Development and evaluation of an open-ended computational evolution system for the genetic analysis of susceptibility to common human diseases. In: European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 129–140. Springer (2008)

    Google Scholar 

  18. Moore, J.H., Greene, C.S., Hill, D.P.: Identification of novel genetic models of glaucoma using the “emergent” genetic programming-based artificial intelligence system. In: R. Riolo, W.P. Worzel, M. Kotanchek (eds.) Genetic Programming Theory and Practice XII, pp. 17–35. Springer International Publishing, Cham (2015)

    CrossRef  Google Scholar 

  19. Moore, J.H., Greene, C.S., Hill, D.P.: Identification of novel genetic models of glaucoma using the “emergent” genetic programming-based artificial intelligence system. In: Genetic Programming Theory and Practice XII, pp. 17–35. Springer (2015)

    Google Scholar 

  20. Moore, J.H., Hill, D.P., Fisher, J.M., Lavender, N., Kidd, L.C.: Human-computer interaction in a computational evolution system for the genetic analysis of cancer. In: R. Riolo, E. Vladislavleva, J.H. Moore (eds.) Genetic Programming Theory and Practice IX, pp. 153–171. Springer New York, New York, NY (2011)

    CrossRef  Google Scholar 

  21. Moore, J.H., Hill, D.P., Saykin, A., Shen, L.: Exploring interestingness in a computational evolution system for the genome-wide genetic analysis of alzheimer’s disease. In: R. Riolo, J.H. Moore, M. Kotanchek (eds.) Genetic Programming Theory and Practice XI, pp. 31–45. Springer New York, New York, NY (2014)

    CrossRef  Google Scholar 

  22. Moore, J.H., White, B.C.: Genome-wide genetic analysis using genetic programming: The critical need for expert knowledge. In: Genetic Programming Theory and Practice IV, pp. 11–28. Springer (2007)

    Google Scholar 

  23. Olson, R.S., Bartley, N., Urbanowicz, R.J., Moore, J.H.: Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. In: GECCO 2016, GECCO ‘16, pp. 485–492. ACM, New York, NY, USA (2016)

    Google Scholar 

  24. Olson, R.S., La Cava, W., Orzeshowski, P., Urbanowicz Ryan J Moore, J.H.: PMLB: A large benchmark suite for machine learning evaluation and comparison. arXiv e-print. https://arxiv.org/abs/1703.00512 (2017)

  25. Olson, R.S., Moore, J.H.: Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool. arXiv e-print. http://arxiv.org/abs/1607.08878 (2016)

  26. Olson, R.S., Moore, J.H.: TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning. JMLR 64, 66–74 (2016)

    Google Scholar 

  27. Olson, R.S., Urbanowicz, R.J., Andrews, P.C., Lavender, N.A., Kidd, L.C., Moore, J.H.: Automating Biomedical Data Science Through Tree-Based Pipeline Optimization. In: G. Squillero, P. Burelli (eds.) Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30–April 1, 2016, Proceedings, Part I, pp. 123–137. Springer International Publishing (2016)

    CrossRef  Google Scholar 

  28. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  29. Ronald, E.M., Sipper, M., Capcarrère, M.S.: Design, observation, surprise! A test of emergence. Artificial Life 5(3), 225–239 (1999)

    CrossRef  Google Scholar 

  30. de Sá, A.G., Pinto, W.J.G., Oliveira, L.O.V., Pappa, G.L.: RECIPE: A Grammar-Based Framework for Automatically Evolving Classification Pipelines. In: European Conference on Genetic Programming, pp. 246–261. Springer (2017)

    Google Scholar 

  31. Silva, S., Muñoz, L., Trujillo, L., Ingalalli, V., Castelli, M., Vanneschi, L.: Multiclass classification through multidimensional clustering. In: Genetic Programming Theory and Practice XIII, pp. 219–239. Springer (2016)

    CrossRef  Google Scholar 

  32. Sipper, M., Fu, W., Ahuja, K., Moore, J.H.: Investigating the parameter space of evolutionary algorithms (2017). arXiv:1706.04119

    Google Scholar 

  33. Sohn, A., Olson, R.S., Moore, J.H.: Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ‘17, pp. 489–496. ACM, New York, NY, USA (2017)

    Google Scholar 

  34. Vanneschi, L., Archetti, F., Castelli, M., Giordani, I.: Classification of oncologic data with genetic programming. Journal of Artificial Evolution and Applications p. 6 (2009)

    Google Scholar 

  35. Zutty, J., Long, D., Adams, H., Bennett, G., Baxter, C.: Multiple objective vector-based genetic programming using human-derived primitives. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1127–1134. ACM (2015)

    Google Scholar 

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Acknowledgements

This work was generously funded by the Perelman School of Medicine and the University of Pennsylvania Health System. Additional funding was provided by National Institutes of Health grants AI116794, DK112217, ES013508, and TR001878.

Author information

Authors and Affiliations

  1. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Randal S. Olson, William La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, Ryan J. Urbanowicz, John H. Holmes & Jason H. Moore

  2. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Moshe Sipper

  3. Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel

    Moshe Sipper

  4. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Patryk Orzechowski

  5. Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Krakow, Poland

    Patryk Orzechowski

Authors
  1. Randal S. Olson
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  2. Moshe Sipper
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  3. William La Cava
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  4. Sharon Tartarone
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  5. Steven Vitale
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  6. Weixuan Fu
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  7. Patryk Orzechowski
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  8. Ryan J. Urbanowicz
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  9. John H. Holmes
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  10. Jason H. Moore
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Corresponding author

Correspondence to Jason H. Moore .

Editor information

Editors and Affiliations

  1. BEACON Center for the Study of Evolution in Action and Department of Computer Science, Michigan State University, East Lansing, Michigan, USA

    Wolfgang Banzhaf

  2. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Randal S. Olson

  3. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    William Tozier

  4. Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan, USA

    Dr. Rick Riolo

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Olson, R.S. et al. (2018). A System for Accessible Artificial Intelligence. In: Banzhaf, W., Olson, R., Tozier, W., Riolo, R. (eds) Genetic Programming Theory and Practice XV. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-90512-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-90512-9_8

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