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A Genetic Programming Approach for the Traffic Signal Control Problem with Epigenetic Modifications

  • Esteban RicaldeEmail author
  • Wolfgang Banzhaf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9594)

Abstract

This paper presents a proof-of-concept for an Epigenetics-based modification of Genetic Programming (GP). The modification is tested with a traffic signal control problem under dynamic traffic conditions.

We describe the new algorithm and show first results. Experiments reveal that GP benefits from properties such as phenotype differentiation, memory consolidation within generations and environmentally-induced change in behavior provided by the epigenetic mechanism. The method can be extended to other dynamic environments.

Keywords

Genetic Programming Epigenetic modification Dynamic environments Traffic signal control 

References

  1. 1.
    Braun, R., Kemper, C.: An evolutionary algorithm for network-wide real-time optimization of traffic signal control. In: 2011 IEEE Forum on Integrated and Sustainable Transportation System (FISTS), pp. 207–214, June 2011Google Scholar
  2. 2.
    Champagne, D.L., Bagot, R.C., van Hasselt, F., Ramakers, G., Meaney, M.J., de Kloet, E.R., Joels, M., Krugers, H.: Maternal care and hippocampal plasticity: evidence for experience-dependent structural plasticity, altered synaptic functioning, and differential responsiveness to glucocorticoids and stress. J. Neurosci. 28(23), 6037–6045 (2008)CrossRefGoogle Scholar
  3. 3.
    Chikumbo, O., Goodman, E., Deb, K.: Approximating a multi-dimensional pareto front for a land use management problem: A modified moea with an epigenetic silencing metaphor. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–9, June 2012Google Scholar
  4. 4.
    Chikumbo, O., Goodman, E., Deb, K.: Triple bottomline many-objective-based decision making for a land use management problem. J. Multi-Criteria Decis. Anal. 22(3–4), 133–159 (2015). http://dx.org/10.1002/mcda.1536 CrossRefGoogle Scholar
  5. 5.
    Day, J.J., Sweatt, J.D.: Epigenetic modifications in neurons are essential for formation and storage of behavioral memory. Neuropsychopharmacology 36(1), 357–358 (2011). http://dx.org/10.1038/npp.2010.125 CrossRefGoogle Scholar
  6. 6.
    Fontana, A.: Epigenetic tracking: biological implications. In: Kampis, G., Karsai, I., Szathmáry, E. (eds.) ECAL 2009, Part I. LNCS, vol. 5777, pp. 10–17. Springer, Heidelberg (2011)Google Scholar
  7. 7.
    Friedrich, B.: Balance and control: Methods for traffic adaptive control. In: World Congress on Intelligent Transport Systems (2nd: 1995: Yokohama-shi, Japan). Steps forward, vol. 5 (1995)Google Scholar
  8. 8.
    Gabor Miklos, G.L., Maleszka, R.: Epigenomic communication systems in humans and honey bees: from molecules to behavior. Horm. Behav. 59(3), 399–406 (2011)CrossRefGoogle Scholar
  9. 9.
    Gershenson, C., Rosenblueth, D.A.: Adaptive selforganization vs static optimization. Kybernetes 41(3/4), 386–403 (2012)CrossRefGoogle Scholar
  10. 10.
    Herrera, C.M., Pozo, M.I., Bazaga, P.: Jack of all nectars, master of most: DNA methylation and the epigenetic basis of niche width in a flower-living yeast. Mol. Ecol. 21(11), 2602–2616 (2012)CrossRefGoogle Scholar
  11. 11.
    Jablonka, E., Lamb, M.: Evolution in Four Dimensions: Genetic, Epigenetic, Behavioral, and Symbolic Variation in the History of Life. Life and Mind. MIT Press, Cambridge (2005). http://books.google.ca/books?id=EaCiHFq3MWsC Google Scholar
  12. 12.
    Nagel, K., Schreckenberg, M.: A cellular automaton model for freeway traffic. J. Phys. I France 2(12), 2221–2229 (1992)CrossRefGoogle Scholar
  13. 13.
    Krubitzer, L., Stolzenberg, D.S.: The evolutionary masquerade: genetic and epigenetic contributions to the neocortex. Curr. Opin. Neurobiol. 24, 157–165 (2014). http://www.sciencedirect.com/science/article/pii/S0959438813002213 CrossRefGoogle Scholar
  14. 14.
    La Cava, W., Helmuth, T., Spector, L., Danai, K.: Genetic programming with epigenetic local search. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO 2015, NY, USA, pp. 1055–1062 (2015). http://doi.acm.org/10.1145/2739480.2754763
  15. 15.
    La Cava, W., Spector, L., Danai, K., Lackner, M.: Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing. In: Proceedings of the 2014 Conference Companion on Genetic and Evolutionary Computation Companion, GECCO Comp 2014, pp. 141–142. ACM, New York (2014)Google Scholar
  16. 16.
    Ledon-Rettig, C.C., Richards, C.L., Martin, L.B.: Epigenetics for behavioral ecologists. Behav. Ecol. 24, 211–324 (2012)Google Scholar
  17. 17.
    Mauro, R., Branco, F.: Update on the statistical analysis of traffic countings on two-lane rural highways. Modern Appl. Sci. 7(6), 67–80 (2013)CrossRefGoogle Scholar
  18. 18.
    Nie, X., Li, Y., Wei, X.: Based on evolutionary algorithm and cellular automata combined traffic signal control. In: 2010 3rd International Symposium on Knowledge Acquisition and Modeling (KAM), pp. 285–288, October 2010Google Scholar
  19. 19.
    Padmasiri, T., Ranasinghe, D.: Genetic programming tuned fuzzy controlled trafficlight system. In: 2014 InternationalConference on Advances in ICT for Emerging Regions (ICTer), pp. 91-95, Dec 2014Google Scholar
  20. 20.
    Sanchez-Medina, J., Galan-Moreno, M., Rubio-Royo, E.: Traffic signal optimization in la almozara district in saragossa under congestion conditions, using genetic algorithms, traffic microsimulation, and cluster computing. IEEE Trans. Intell. Transp. Syst. 11(1), 132–141 (2010)CrossRefGoogle Scholar
  21. 21.
    Sousa, J., Costa, E.: Epial - an epigenetic approach for an artificial life model. In: International Conference on Agents and Artificial Intelligence (2010)Google Scholar
  22. 22.
    Tanev, I., Yuta, K.: Implications of epigenetic learning via modification of histones on performance of genetic programming. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 213–224. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  23. 23.
    Turner, A.P., Lones, M.A., Fuente, L.A., Stepney, S., Caves, L.S., Tyrrell, A.M.: The incorporation of epigenetics in artificial gene regulatory networks. BioSystems 112(2), 56–62 (2013)CrossRefGoogle Scholar
  24. 24.
    Wang, F.Y.: Parallel control and management for intelligent transportation systems: concepts, architectures, and applications. IEEE Trans. Intell. Transp. Syst. 11, 630–638 (2010)CrossRefGoogle Scholar
  25. 25.
    Zhang, M., Zhao, S., Lv, J., Qian, Y.: Multi-phase urban traffic signal real-time control with multi-objective discrete differential evolution. In: 2009 International Conference on Electronic Computer Technology, pp. 296–300, February 2009Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Memorial University of NewfoundlandSt. John’sCanada

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