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International Conference on the Applications of Evolutionary Computation (Part of EvoStar)

EvoApplications 2022: Applications of Evolutionary Computation pp 269–282Cite as

WebGE: An Open-Source Tool for Symbolic Regression Using Grammatical Evolution

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13224))

Abstract

Many frameworks and libraries are available for researchers working on optimization. However, the majority of them require programming knowledge, lack of a friendly user interface and cannot be run on different operating systems. WebGE is a new optimization tool which provides a web-based graphical user interface allowing any researcher to use Grammatical Evolution and Differential Evolution on symbolic regression problems. In addition, the fact that it can be deployed on any server as a web service also incorporating user authentication, makes it a versatile and portable tool that can be shared by multiple researchers. Finally, the modular software architecture allows to easily extend WebGE to other algorithms and types of problems.

This work has been partially supported by the Spanish Ministerio de Ciencia, Innovación y Universidades (MCIU/AEI/FEDER, UE) under grants ref. PGC2018-095322-B-C22 and RTI2018-095180-B-I00; and Comunidad de Madrid y Fondos Estructurales de la Unión Europea with grants ref. P2018/TCS-4566, B2017/BMD3773 (GenObIA-CM) and Y2018/NMT-4668 (Micro-Stress - MAP-CM).

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References

  1. Adaptive and Bioinspired Systems Group: ABSys JECO (Java Evolutionary COmputation) library. https://github.com/ABSysGroup/jeco. Accessed 2021

  2. Asseg, F., Chatterjee, S.: exp4j: a library for expression evaluation in Java. https://www.objecthunter.net/exp4j/index.html. Accessed 2021

  3. Assunção, F., Lourenço, N., Ribeiro, B., Machado, P.: Evolution of scikit-learn pipelines with dynamic structured grammatical evolution. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds.) EvoApplications 2020. LNCS, vol. 12104, pp. 530–545. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43722-0_34

    Chapter  Google Scholar 

  4. Augusto, D.A., Barbosa, H.J., Barreto, A.M., Bernardino, H.S.: A new approach for generating numerical constants in grammatical evolution. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 193–194 (2011)

    Google Scholar 

  5. Boettiger, C.: An introduction to Docker for reproducible research. ACM SIGOPS Oper. Syst. Rev. 49(1), 71–79 (2015)

    Article  Google Scholar 

  6. Charte, F., Vico, A., Pérez-Godoy, M.D., Rivera, A.J.: predtoolsTS: R package for streamlining time series forecasting. Prog. Artif. Intell. 8(4), 505–510 (2019). https://doi.org/10.1007/s13748-019-00193-z

    Article  Google Scholar 

  7. Coletti, M.A., Scott, E.O., Bassett, J.K.: Library for evolutionary algorithms in Python (LEAP). In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 1571–1579 (2020)

    Google Scholar 

  8. Colmenar, J., Hidalgo, J., Salcedo-Sanz, S.: Automatic generation of models for energy demand estimation using grammatical evolution. Energy 164, 183–193 (2018)

    Article  Google Scholar 

  9. Davis, A.L.: Spring data. In: Spring Quick Reference Guide, pp. 43–59. Apress, Berkeley, CA (2020). https://doi.org/10.1007/978-1-4842-6144-6_6

    Chapter  Google Scholar 

  10. De Smet, G., open source contributors: OptaPlanner User Guide. Red Hat, Inc. or third-party contributors (2006). https://www.optaplanner.org. Accessed 2021

  11. Dempsey, I., O’Neill, M., Brabazon, A.: Constant creation and adaptation in grammatical evolution. In: Foundations in Grammatical Evolution for Dynamic Environments, vol. 194, pp. 69–104. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00314-1_5

  12. Durillo, J.J., Nebro, A.J.: jMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42(10), 760–771 (2011)

    Article  Google Scholar 

  13. Elyasaf, A., Sipper, M.: Software review: the HeuristicLab framework. Genet. Program. Evolvable Mach. 15(2), 215–218 (2014). https://doi.org/10.1007/s10710-014-9214-4

    Article  Google Scholar 

  14. Estévez-Velarde, S., Gutiérrez, Y., Almeida-Cruz, Y., Montoyo, A.: General-purpose hierarchical optimisation of machine learning pipelines with grammatical evolution. Inf. Sci. 543, 58–71 (2021)

    Article  Google Scholar 

  15. Fushiki, T.: Estimation of prediction error by using k-fold cross-validation. Stat. Comput. 21(2), 137–146 (2011)

    Article  MathSciNet  Google Scholar 

  16. Hidalgo, J.I., et al.: Glucose forecasting combining Markov chain based enrichment of data, random grammatical evolution and bagging. Appl. Soft Comput. 88, 105923 (2020)

    Article  Google Scholar 

  17. Johansson, L., Dossot, D.: RabbitMQ Essentials: Build Distributed and Scalable Applications with Message Queuing Using RabbitMQ. Packt Publishing Ltd., Hawthorne, USA (2020)

    Google Scholar 

  18. Martínez-Rodríguez, D., Colmenar, J.M., Hidalgo, J.I., Villanueva Micó, R.J., Salcedo-Sanz, S.: Particle swarm grammatical evolution for energy demand estimation. Energy Sci. Eng. 8(4), 1068–1079 (2020)

    Article  Google Scholar 

  19. Mauceri, S., Sweeney, J., McDermott, J.: One-class subject authentication using feature extraction by grammatical evolution on accelerometer data. In: Yalaoui, F., Amodeo, L., Talbi, E.-G. (eds.) Heuristics for Optimization and Learning. SCI, vol. 906, pp. 393–407. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-58930-1_26

    Chapter  Google Scholar 

  20. Meyer, M.: Continuous integration and its tools. IEEE Softw. 31(3), 14–16 (2014)

    Article  Google Scholar 

  21. Nicolau, M., Agapitos, A.: Understanding grammatical evolution: grammar design. In: Ryan, C., O’Neill, M., Collins, J.J. (eds.) Handbook of Grammatical Evolution, pp. 23–53. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78717-6_2

    Chapter  Google Scholar 

  22. O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Norwell (2003)

    Book  Google Scholar 

  23. Qaddoura, R., Faris, H., Aljarah, I., Castillo, P.A.: EvoCluster: an open-source nature-inspired optimization clustering framework. SN Comput. Sci. 2(3), 1–12 (2021)

    Article  Google Scholar 

  24. Ramírez, A., Romero, J.R., García-Martínez, C., Ventura, S.: JCLEC-MO: a Java suite for solving many-objective optimization engineering problems. Eng. Appl. Artif. Intell. 81, 14–28 (2019)

    Article  Google Scholar 

  25. Red Gate Software Ltd: Flyway open-source database migration tool. https://flywaydb.org/. Accessed 2021

  26. Scott, E.O., Luke, S.: ECJ at 20: toward a general metaheuristics toolkit. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1391–1398 (2019)

    Google Scholar 

  27. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  28. Tian, Y., Cheng, R., Zhang, X., Jin, Y.: PlatEMO: a MATLAB platform for evolutionary multi-objective optimization. IEEE Comput. Intell. Mag. 12(4), 73–87 (2017)

    Article  Google Scholar 

  29. Vladislavleva, E.J., Smits, G.F., Den Hertog, D.: Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming. IEEE Trans. Evol. Comput. 13(2), 333–349 (2008)

    Article  Google Scholar 

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Authors and Affiliations

  1. Rey Juan Carlos University, 28933, Móstoles, Spain

    J. Manuel Colmenar & Raúl Martín-Santamaría

  2. Complutense University, 28040, Madrid, Spain

    J. Ignacio Hidalgo

Authors
  1. J. Manuel Colmenar
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  2. Raúl Martín-Santamaría
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  3. J. Ignacio Hidalgo
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Correspondence to J. Manuel Colmenar .

Editor information

Editors and Affiliations

  1. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

  2. Universidad Complutense de Madrid, Madrid, Spain

    J. Ignacio Hidalgo

  3. Edinburgh Napier University, Edinburgh, UK

    Kehinde Oluwatoyin Babaagba

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Colmenar, J.M., Martín-Santamaría, R., Hidalgo, J.I. (2022). WebGE: An Open-Source Tool for Symbolic Regression Using Grammatical Evolution. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_18

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  • DOI: https://doi.org/10.1007/978-3-031-02462-7_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02461-0

  • Online ISBN: 978-3-031-02462-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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