Skip to main content
SpringerLink
Log in
Book cover

Genetic Programming Theory and Practice XIX pp 81–90Cite as

Genetic Programming for Interpretable and Explainable Machine Learning

  • Chapter
  • First Online:

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

Abstract

Increasing demand for human understanding of machine decision-making is deemed crucial for machine learning (ML) methodology development and further applications. It has inspired the emerging research field of interpretable and explainable ML/AI. Techniques have been developed to either provide additional explanations to a trained ML model or learn innately compact and understandable models. Genetic programming (GP), as a powerful learning instrument, holds great potential in interpretable and explainable learning. In this chapter, we first discuss concepts and popular methods in interpretable and explainable ML, and review research using GP for interpretability and explainability. We then introduce our previously proposed GP-based framework for interpretable and explainable learning applied to bioinformatics.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Adel, T., Ghahramani, Z., Weller, A.: Discovering interpretable representations for both deep generative and discriminative models. In: Proceedings of the 35th International Conference on Machine Learning, vol. 80, pp. 50–59 (2018)

    Google Scholar 

  2. Brameier, M.F., Banzhaf, W.: Linear Genetic Programming. Springer (2007)

    Google Scholar 

  3. Breiman, L.: Random Forest. Mach. Learn. 45, 5–32 (2001)

    Article  MATH  Google Scholar 

  4. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  5. Evans, B.P., Xue, B., Zhang, M.: What’s inside the black box? a genetic programming method for interpreting complex machine learning models. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 1012–1020 (2019)

    Google Scholar 

  6. Ferreira, L.A., Guimarães, F.G., Silva, R.: Applying genetic programming to improve interpretability in machine learning models. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2020)

    Google Scholar 

  7. Gonzalez-Dominguez, R., Sayago, A., Fernandez-Recamales, A.: Metabolomics in Alzheimer’s disease: The need of complementaryanalytical platforms for the identification of biomarkers to unravel theunderlying pathology. J. Chromatogr. B 1071, 75–92 (2017)

    Article  Google Scholar 

  8. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer (2001)

    Google Scholar 

  9. Hein, D., Udluft, S., Runkler, T.A.: Interpretable policies for reinforcement learning by genetic programming. Eng. Appl. Artif. Intell. 76, 158–169 (2018)

    Article  Google Scholar 

  10. Hu, T., Banzhaf, W.: Neutrality, robustness, and evolvability in genetic programming. In: Riolo, R., Worzel, B., Goldman, B., Tozier, B. (eds.) Genetic Programming Theory and Practice XIV, chap. 7, pp. 101–117. Springer (2018)

    Google Scholar 

  11. Hu, T., Banzhaf, W., Moore, J.H.: Population exploration on genotype networks in genetic programming. In: Proceedings of the 13th International Conference on Parallel Problem Solving from Nature (PPSN), Lecture Notes in Computer Science, vol. 8672, pp. 424–433 (2014)

    Google Scholar 

  12. Hu, T., Oksanen, K., Zhang, W., Randell, E., Furey, A., Sun, G., Zhai, G.: An evolutionary learning and network approach to identifying key metabolites for osteoarthritis. PLoS Comput. Biol. 14(3), e1005,986 (2018)

    Google Scholar 

  13. Hu, T., Payne, J.L., Banzhaf, W., Moore, J.H.: Evolutionary dynamics on multiple scales: a quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming. Genet. Program Evol. Mach. 13, 305–337 (2012)

    Article  Google Scholar 

  14. Hu, T., Tomassini, M., Banzhaf, W.: A network perspective on genotype-phenotype mapping in genetic programming. Genet. Program Evol. Mach. 21, 375–397 (2020)

    Article  Google Scholar 

  15. Javed, N., Gobet, F., Lane, P.: Simplification of genetic programs: a literature survey. In: Data Mining and Knowledge Discovery (2022). https://doi.org/10.1007/s10,618-022-00,830-7

  16. Kim, B., Khanna, R., Koyejo, O.O.: Examples are not enough, learn to criticize! Criticism for interpretability. In: Proceedings of the 13th Conference on Neural Information Processing Systems (NeurIPS), vol. 29 (2016)

    Google Scholar 

  17. Krawiec, K.: Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genet. Program. Evol. Mach. 3(329–343) (2002)

    Google Scholar 

  18. Lee, M., Hu, T.: Computational methods for the discovery of metabolic markers of complex traits. Metabolites 9(4), 66 (2019)

    Article  Google Scholar 

  19. Lensen, A., Xue, B., Zhang, M.: Genetic programming for evolving a front of interpretable models for data visualization. IEEE Trans. Cybern. 51(11), 5468–5482 (2021)

    Article  Google Scholar 

  20. Li, Z., He, J., Zhang, X., Fu, H., Qin, J.: Toward high accuracy and visualization: an interpretable feature extraction method based on genetic programming and non-overlap degree. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 299–304 (2020)

    Google Scholar 

  21. Lipton, Z.C.: The mythos of model interpretability. Commun. ACM 61(10), 36–43 (2018)

    Article  Google Scholar 

  22. Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS), pp. 4768–4777 (2017)

    Google Scholar 

  23. Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1–38 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  24. Molnar, C.: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. leanpub.com (2022)

    Google Scholar 

  25. Murdoch, W.J., Singh, C., Kumbier, K., Abbasi-Asl, R., Yu, B.: Interpretable machine learning: definitions, methods, and applications. Proc. Natl. Acad. Sci. 116(44), 22071–22080 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  26. Parziale, A., Senatore, R., Cioppa, A., Marcelli, A.: Cartesian genetic programming for diagnosis of Parkinson disease through handwriting analysis: Performance vs. interpretability issues. Artif. Intell. Med. 111, 101,984 (2021)

    Google Scholar 

  27. Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should I trust you?”: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)

    Google Scholar 

  28. Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019)

    Article  Google Scholar 

  29. Samek, W., Montavon, G., Lapuschkin, S., Anders, C.J., Muller, K.R.: Explaining deep neural networks and beyond: a review of methods and applications. Proc. IEEE 109(3), 247–278 (2021)

    Article  Google Scholar 

  30. Sha, C., Cuperlovic-Culf, M., Hu, T.: SMILE: systems metabolomics using interpretable learning and evolution. BMC Bioinform. 22, 284 (2021)

    Article  Google Scholar 

  31. Wang, G., Zhou, Y., Huang, F.J., Tang, H.D., Xu, X.H., Liu, J.J., Wang, Y., Deng, Y.L., Ren, R.J., Xu, W., Ma, J.F., Zhang, Y.N., Zhao, A.H., Chen, S.D., Jia, W.: Plasma metabolite profiles of Alzheimer’s disease and mild cognitive impairment. J. Proteome Res. 133, 2649–2658 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, Queen’s University, Kingston, ON, Canada

    Ting Hu

  2. Department of Computer Science, Memorial University, St. John’s, NL, Canada

    Ting Hu

Authors
  1. Ting Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ting Hu .

Editor information

Editors and Affiliations

  1. Engineering Sciences Graduate Program, Tecnológico Nacional de México/Instituto Tecnológico de Tijuana, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  2. Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Wels, Austria

    Stephan M. Winkler

  3. Department of Informatics, Faculty of Sciences, University of Lisbon, Lisbon, Portugal

    Sara Silva

  4. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hu, T. (2023). Genetic Programming for Interpretable and Explainable Machine Learning. In: Trujillo, L., Winkler, S.M., Silva, S., Banzhaf, W. (eds) Genetic Programming Theory and Practice XIX. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-19-8460-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-8460-0_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8459-4

  • Online ISBN: 978-981-19-8460-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation