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A new genetic programming framework based on reaction systems

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Abstract

This paper presents a new genetic programming framework called Evolutionary Reaction Systems. It is based on a recently defined computational formalism, inspired by chemical reactions, called Reaction Systems, and it has several properties that distinguish it from other existing genetic programming frameworks, making it interesting and worthy of investigation. For instance, it allows us to express complex constructs in a simple and intuitive way, and it lightens the final user from the task of defining the set of primitive functions used to build up the evolved programs. Given that Evolutionary Reaction Systems is new and it has small similarities with other existing genetic programming frameworks, a first phase of this work is dedicated to a study of some important parameters and their influence on the algorithm’s performance. Successively, we use the best parameter setting found to compare Evolutionary Reaction Systems with other well established machine learning methods, including standard tree-based genetic programming. The presented results show that Evolutionary Reaction Systems are competitive with, and in some cases even better than, the other studied methods on a wide set of benchmarks.

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References

  1. T. Bäck, R. Breukelaar. Using genetic algorithms to evolve behavior in cellular automata. ed. by C. Calude, M. Dinneen, G. Paun, M. Pérez-Jiménez, G. Rozenberg. UC, Lecture Notes in Computer Science, vol. 3699. (Springer, 2005) pp. 1–10

  2. M. Castelli, L. Manzoni, L. Vanneschi. Parameter tuning of evolutionary reactions systems. Genetic and Evolutionary Computation Conference, GECCO 2012, (ACM, Philadelphia 2012) pp. 727–734

  3. M. Caudill. Neural networks primer, part I. AI Expert 2, 46–52 (1987)

    Google Scholar 

  4. N. Cristianini, J. Shawe-Taylor. An introduction to support vector machines: and other kernel-based learning methods. (Cambridge University Press, Cambridge, 2000)

    Book  Google Scholar 

  5. A. Ehrenfeucht, G. Rozenberg. Basic notions of reaction systems. Developments in Language Theory 8th International Conference, DLT 2004, LNCS, vol. 3340. (Springer, 2004) pp. 27–29

  6. A. Ehrenfeucht, G. Rozenberg. Reaction systems. Fundamenta Informaticae 75, 263–280 (2007)

    MathSciNet  MATH  Google Scholar 

  7. A. Ehrenfeucht, G. Rozenberg. Introducing time in reaction systems. Theoret. Comput. Sci. 410, 310–322 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  8. D. Fogel. Evolving computer programs. ed. by D. Fogel. Evolutionary Computation: The Fossil Record, chap. 5. (MIT Press, Cambridge, 1998) pp. 143–144

    Chapter  Google Scholar 

  9. L. Fogel, A. Owens, M. Walsh. Artificial Intelligence Through Simulated Evolution. (Wiley, New York, 1966)

    MATH  Google Scholar 

  10. R. Friedberg. A learning machine: Part 1. IBM J. Res. Dev. 2:1, 2–13 (1958)

    MathSciNet  Google Scholar 

  11. D. Heckerman. A tutorial on learning with bayesian networks. Innovations in Bayesian Networks, Studies in Computational Intelligence, vol. 156, (Springer, Berlin, 2008) pp. 33–82

  12. W. Kantschik, W. Banzhaf. Linear-tree GP and its comparison with other GP structures. Genetic Programming, Proceedings of EuroGP’2001, LNCS, vol. 2038. (Springer, Lake Como, 2001) pp. 302–312

  13. W. Kantschik, W. Banzhaf. Linear-graph GP—A new GP structure. In: Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002, LNCS, vol. 2278, pp. 83–92. (Springer, Kinsale, 2002)

  14. J. Koza. A hierarchical approach to learning the boolean multiplexer function. ed. by G.J.E. Rawlins. Foundations of genetic algorithms. (Morgan Kaufmann, Indiana University, 1991) pp. 171–192

  15. J. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. (MIT Press, Cambridge, 1992)

    MATH  Google Scholar 

  16. L. Manzoni, M. Castelli, L. Vanneschi. Evolutionary reaction systems. ed. by M. Giacobini, L. Vanneschi, W.S. Bush. Evolutionary Computation, Machine Learning and Data Mining in Computational Biology, EvoBIO 2012, Lecture Notes in Computer Science, vol. 7246. (Springer, Màlaga, Spain, 2012) pp. 13–25

  17. J. McDermott, D.R. White, S. Luke, L. Manzoni, M. Castelli, L. Vanneschi, W. Jaskowski, K. Krawiec, R. Harper, K.D. Jong, U.M. OReilly. Genetic programming needs better benchmarks. Genetic and Evolutionary Computation Conference, GECCO 2012. (ACM, Philadelphia, 2012) pp. 791–798

  18. J. Miller, P. Thomson. Cartesian genetic programming. Genetic Programming, Proceedings of EuroGP’2000, LNCS, vol. 1802. (Springer, Edinburgh, 2000) pp. 121–132

  19. D. Montana. Strongly typed genetic programming. Evolutionary Computation 3(2), 199–230 (1995)

    Article  Google Scholar 

  20. M. O’Neill, C. Ryan. Grammatical evolution. IEEE Trans. Evol. Comput. 5(4), 349–358 (2001)

    Article  Google Scholar 

  21. M. O’Neill, L. Vanneschi, S. Gustafson, W. Banzhaf. Open issues in genetic programming. Genet. Program. Evolvable Mach. 11, 339–363 (2010)

    Article  Google Scholar 

  22. M. Orr. Introduction to radial basis function networks. (Tech. rep., Centre For Cognitive Science, University of Edinburgh, Edinburgh, Scotland, 1996)

  23. J. Platt. A fast algorithm for training support vector machines. (Tech. rep., Microsoft Research, Redmond, USA, 1998)

  24. R. Poli, W. Langdon, N. McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (2008). http://www.gp-field-guide.org.uk. (With contributions by J. R. Koza)

  25. I. Rish. An empirical study of the naive bayes classifier. IJCAI-01 workshop on “Empirical Methods in AI” (2001)

  26. S. Smit, A. Eiben. Comparing parameter tuning methods for evolutionary algorithms. In: Evolutionary Computation, 2009. CEC ’09. IEEE Congress on (2009) pp. 399–406

  27. A. Teller, M. Veloso, in PADO: A New Learning Architecture for Object Recognition, ed. by K. Ikeuchi, M. Veloso. Symbolic Visual Learning. (Oxford University Press, Oxford, 1996), pp. 81–116

  28. P.A. Whigham. Grammatical bias for evolutionary learning. Ph.D. thesis, School of Computer Science, University College, University of New South Wales, Australian Defence Force Academy, Canberra, Australia (14 October 1996)

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Authors and Affiliations

  1. Dipartimento di Informatica, Sistemistica e Comunicazione (D.I.S.Co.), Università di Milano-Bicocca, 20126, Milan, Italy

    Luca Manzoni & Leonardo Vanneschi

  2. ISEGI, Universidade Nova de Lisboa, 1070-312, Lisboa, Portugal

    Mauro Castelli & Leonardo Vanneschi

  3. INESC-ID, IST / Universidade Técnica de Lisboa, 1000-029, Lisboa, Portugal

    Leonardo Vanneschi

Authors
  1. Luca Manzoni
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  2. Mauro Castelli
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  3. Leonardo Vanneschi
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Correspondence to Leonardo Vanneschi.

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Manzoni, L., Castelli, M. & Vanneschi, L. A new genetic programming framework based on reaction systems. Genet Program Evolvable Mach 14, 457–471 (2013). https://doi.org/10.1007/s10710-013-9184-y

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  • DOI: https://doi.org/10.1007/s10710-013-9184-y

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