GELAB - A Matlab Toolbox for Grammatical Evolution

  • Muhammad Adil RajaEmail author
  • Conor Ryan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11315)


In this paper, we present a Matlab version of libGE. libGE is a famous library for Grammatical Evolution (GE). GE was proposed initially in [1] as a tool for automatic programming. Ever since then, GE has been widely successful in innovation and producing human-competitive results for various types of problems. However, its implementation in C++ (libGE) was somewhat prohibitive for a wider range of scientists and engineers. libGE requires several tweaks and integrations before it can be used by anyone. For anybody who does not have a background in computer science, its usage could be a bottleneck. This prompted us to find a way to bring it to Matlab. Matlab, as it is widely known, is a fourth generation programming language used for numerical computing. Details aside, but it is well known for its user-friendliness in the wider research community. By bringing GE to Matlab, we hope that many researchers across the world shall be able to use it, despite their academic background. We call our implementation of GE as GELAB. GELAB is currently present online as an open-source software ( It can be readily used in research and development.


  1. 1.
    O’Neill, M., Ryan, C.: Grammatical evolution. IEEE Trans. Evol. Comput. 5, 349–358 (2001)CrossRefGoogle Scholar
  2. 2.
    Müller, V.C., Bostrom, N.: Future progress in artificial intelligence: a survey of expert opinion. In: Müller, V.C. (ed.) Fundamental Issues of Artificial Intelligence. SL, vol. 376, pp. 553–570. Springer, Cham (2016). Scholar
  3. 3.
    Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)zbMATHGoogle Scholar
  4. 4.
    Raja, M.A., Rahman, S.U.: A tutorial on simulating unmanned aerial vehicles. In: 2017 International Multi-topic Conference (INMIC), pp. 1–6 (2017)Google Scholar
  5. 5.
    Habib, S., Malik, M., Rahman, S.U., Raja, M.A.: NUAV - a testbed for developing autonomous unmanned aerial vehicles. In: 2017 International Conference on Communication, Computing and Digital Systems (C-CODE), pp. 185–192 (2017)Google Scholar
  6. 6.
    Raja, M.A., Ali, S., Mahmood, A.: Simulators as drivers of cutting edge research. In: 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pp. 114–119 (2016)Google Scholar
  7. 7.
    Keijzer, M.: Scaled symbolic regression. Genet. Program. Evolvable Mach. 5, 259–269 (2004)CrossRefGoogle Scholar
  8. 8.
    Raja, A., Flanagan, C.: Real-time, non-intrusive speech quality estimation: a signal-based model. In: O’Neill, M., et al. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 37–48. Springer, Heidelberg (2008). Scholar
  9. 9.
    Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE Trans. Evol. Comput. 3, 287–297 (1999)CrossRefGoogle Scholar
  10. 10.
    Mininno, E., Cupertino, F., Naso, D.: Real-valued compact genetic algorithms for embedded microcontroller optimization. IEEE Trans. Evol. Comput. 12, 203–219 (2008)CrossRefGoogle Scholar
  11. 11.
    Keijzer, M.: Alternatives in subtree caching for genetic programming. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 328–337. Springer, Heidelberg (2004). Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Information SystemsUniversity of LimerickLimerickIreland

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