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Using Modularity Metrics as Design Features to Guide Evolution in Genetic Programming

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Genetic Programming Theory and Practice XVII

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

Abstract

Genetic Programming has advanced the state of the art in the field of software synthesis. However, it has still not been able to produce some of the more complex programs routinely written by humans. One of the heuristics human programmers use to build complex software is the organization of code into reusable modules. Ever since the introduction of the concept of Automatically Defined Functions (ADFs) by John Koza in the 1990s, the genetic programming community has also expressed the need to evolve modular programs, but despite this interest and several subsequent innovations, the goal of evolving large-scale software built on reusable modules has not yet been achieved. In this chapter, we first discuss two modularity metrics—Reuse and Repetition—and describe the procedure for calculating them from program code and corresponding execution traces. We then introduce the concept of design features, which can be used alongside error measures to guide evolution. We also demonstrate the use of modularity design features in parent selection.

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Notes

  1. 1.

    https://github.com/lspector/Clojush.

References

  1. Clune, J., Mouret, J. B., & Lipson, H. (2013). The evolutionary origins of modularity. Proceedings of the Royal Society B: Biological Sciences, 280(1755), 20122863.

    Article  Google Scholar 

  2. Helmuth, T., & Spector, L. (2015, July). General program synthesis benchmark suite. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (pp. 1039–1046). ACM.

    Google Scholar 

  3. Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection (Vol. 1). MIT press.

    Google Scholar 

  4. Koza, J. R. (1994). Architecture-altering operations for evolving the architecture of a multi-part program in genetic programming.

    Google Scholar 

  5. Angeline, P. J., & Pollack, J. (1993, February). Evolutionary module acquisition. In Proceedings of the second annual conference on evolutionary programming (pp. 154–163).

    Google Scholar 

  6. Spector, L. (1995). Evolving Control Structures with Automatically Defined Macros. Submitted to the 1995 AAAI Fall Symposium on Genetic Programming.

    Google Scholar 

  7. Banzhaf, W., Banscherus, D., & Dittrich, P. (1999). Hierarchical genetic programming using local modules. Secretary of the SFB 531.

    Google Scholar 

  8. Dhama, H. (1995). Quantitative models of cohesion and coupling in software. Journal of Systems and Software, 29(1), 65–74.

    Article  Google Scholar 

  9. Saini, A. K., & Spector, L. (2019). Modularity Metrics for Genetic Programming. In Genetic and Evolutionary Computation Conference Companion (GECCO 2019 Companion), July 13–17, 2019, Prague, Czech Republic. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3319619.3326908

  10. Callebaut, W., Rasskin-Gutman, D., & Simon, H. A. (Eds.). (2005). Modularity: understanding the development and evolution of natural complex systems. MIT press.

    Google Scholar 

  11. O’Neill, M., Vanneschi, L., Gustafson, S., & Banzhaf, W. (2010). Open issues in genetic programming. Genetic Programming and Evolvable Machines, 11(3–4), 339–363.

    Article  Google Scholar 

  12. Gerules, G., Janikow, C. (2016, July). A survey of modularity in genetic programming. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 5034–5043). IEEE.

    Google Scholar 

  13. Lee Spector. 2001. Autoconstructive evolution: Push, pushGP, and pushpop. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO- 2001), Vol. 137.

    Google Scholar 

  14. Spector, L., Martin, B., Harrington, K., Helmuth, T. (2011, July). Tag-based modules in genetic programming. In Proceedings of the 13th annual conference on Genetic and evolutionary computation (pp. 1419–1426). ACM.

    Google Scholar 

  15. Helmuth, T., Spector, L., & Matheson, J. (2015). Solving uncompromising problems with lexicase selection. IEEE Transactions on Evolutionary Computation, 19(5), 630–643.

    Article  Google Scholar 

  16. La Cava, W., Spector, L., & Danai, K. (2016, July). Epsilon-lexicase selection for regression. In Proceedings of the Genetic and Evolutionary Computation Conference 2016 (pp. 741–748). ACM.

    Google Scholar 

  17. Lalejini, A., Ofria, C. (2018, July). Evolving event-driven programs with SignalGP. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 1135–1142). ACM

    Google Scholar 

  18. Krzysztof Krawiec and Bartosz Wieloch. 2009. Functional modularity for genetic programming. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation. ACM, 995–1002.

    Google Scholar 

  19. Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23), 8577–8582.

    Article  Google Scholar 

  20. Qin, Z., McKay, R., & Gedeon, T. (2018). Why don’t the modules dominate-Investigating the Structure of a Well-Known Modularity-Inducing Problem Domain. arXiv preprint arXiv:1807.05976.

    Google Scholar 

  21. Helmuth, T., McPhee, N. F., & Spector, L. (2018, July). Program synthesis using uniform mutation by addition and deletion. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 1127–1134). ACM.

    Google Scholar 

  22. Helmuth, T., McPhee, N. F., Pantridge, E., & Spector, L. (2017, July). Improving generalization of evolved programs through automatic simplification. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 937–944). ACM.

    Google Scholar 

  23. Zhan, H. (2014, July). A quantitative analysis of the simplification genetic operator. In Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation (pp. 1077–1080). ACM.

    Google Scholar 

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Acknowledgements

We would like to thank other members of Hampshire College Institute for Computational Intelligence for their valuable inputs.

This material is based upon work supported by the National Science Foundation under Grant No. 1617087. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Author information

Authors and Affiliations

  1. College of Information and Computer Sciences, University of Massachusetts, Amherst, MA, USA

    Anil Kumar Saini & Lee Spector

  2. Department of Computer Science, Amherst College, Amherst, MA, USA

    Lee Spector

  3. School of Cognitive Science, Hampshire College, Amherst, MA, USA

    Lee Spector

Authors
  1. Anil Kumar Saini
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  2. Lee Spector
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Corresponding author

Correspondence to Anil Kumar Saini .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, John R. Koza Chair, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. BEACON Center, Michigan State University, East Lansing, MI, USA

    Erik Goodman

  3. Department of Computer Science and Engineering, Michigan State University, Okemos, MI, USA

    Leigh Sheneman

  4. Depto Ingenieria en Electronic Electrica TecnolĂłgico Nacional de MĂ©xico/ IT, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  5. Evolution Enterprises, Ann Arbor, MI, USA

    Bill Worzel

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Saini, A.K., Spector, L. (2020). Using Modularity Metrics as Design Features to Guide Evolution in Genetic Programming. In: Banzhaf, W., Goodman, E., Sheneman, L., Trujillo, L., Worzel, B. (eds) Genetic Programming Theory and Practice XVII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-030-39958-0_9

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  • DOI: https://doi.org/10.1007/978-3-030-39958-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39957-3

  • Online ISBN: 978-3-030-39958-0

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