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Genetic Programming Theory and Practice XVII pp 25–38Cite as

It Is Time for New Perspectives on How to Fight Bloat in GP

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

Abstract

The present and future of evolutionary algorithms depends on the proper use of modern parallel and distributed computing infrastructures. Although still sequential approaches dominate the landscape, available multi-core, many-core and distributed systems will make users and researchers to more frequently deploy parallel version of the algorithms. In such a scenario, new possibilities arise regarding the time saved when parallel evaluation of individuals are performed. And this time saving is particularly relevant in Genetic Programming. This paper studies how evaluation time influences not only time to solution in parallel/distributed systems, but may also affect size evolution of individuals in the population, and eventually will reduce the bloat phenomenon GP features. This paper considers time and space as two sides of a single coin when devising a more natural method for fighting bloat. This new perspective allows us to understand that new methods for bloat control can be derived, and the first of such a method is described and tested. Experimental data confirms the strength of the approach: using computing time as a measure of individuals’ complexity allows to control the growth in size of genetic programming individuals.

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  • DOI: 10.1007/978-3-030-39958-0_2
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References

  1. Dignum, S., Poli, R.: Operator equalisation and bloat free GP. In: European Conference on Genetic Programming, LNCS, vol. 4971, pp. 110–121. Springer (2008)

    Google Scholar 

  2. Galeano, G., Fernández de Vega, F., Tomassini, M., Vanneschi, L.: Studying the influence of synchronous and asynchronous parallel GP on programs length evolution. In: Congress on Evolutionary Computation, vol. 2, pp. 1727–1732. IEEE (2002)

    Google Scholar 

  3. Koza, J.R.: Genetic programming: On the programming of computers by means of natural selection. MIT Press (1992)

    Google Scholar 

  4. Langdon, W.B., Poli, R.: Fitness causes bloat, pp. 13–22. Soft Computing in Engineering Design and Manufacturing. Springer (1998)

    Google Scholar 

  5. Luke, S., Panait, L.: Lexicographic parsimony pressure. In: Conference on Genetic and Evolutionary Computation, pp. 829–836 (2002)

    Google Scholar 

  6. Luke, S., Panait, L.: A comparison of bloat control methods for genetic programming. Evolutionary Computation 14(3), 309–344 (2006)

    CrossRef  Google Scholar 

  7. Osman, A., Ammar, H.: Dynamic load balancing strategies for parallel computers. In: International Symposium on Parallel and Distributed Computing, vol. 11, pp. 110–120 (2002)

    Google Scholar 

  8. Oussaidène, M., Chopard, B., Pictet, O.V., Tomassini, M.: Parallel genetic programming and its application to trading model induction. Parallel Computing 23(8), 1183–1198 (1997)

    CrossRef  Google Scholar 

  9. Silva, S.: Reassembling operator equalisation: A secret revealed. ACM SIGEVOlution 5(3), 10–22 (2011)

    CrossRef  Google Scholar 

  10. Silva, S., Costa, E.: Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories. Genetic Programming and Evolvable Machines 10(2), 141–179 (2009)

    CrossRef  Google Scholar 

  11. Trujillo, L., Munoz, L., Galván-López, E., Silva, S.: Neat genetic programming: Controlling bloat naturally. Information Sciences (2016)

    Google Scholar 

  12. Vanneschi, L., Castelli, M., Silva, S.: Measuring bloat, overfitting and functional complexity in genetic programming. In: Conference on Genetic and Evolutionary Computation, pp. 877–884. ACM (2010)

    Google Scholar 

  13. Fernández de Vega, F.: Distributed genetic programming models with application to logic synthesis on FPGAs. Ph.D. thesis, University of Extremadura (2001)

    Google Scholar 

  14. Fernández de Vega, F., Abengózar Sánchez, J.G., Cotta, C.: A preliminary analysis and simulation of load balancing techniques applied to parallel genetic programming. In: International Work-Conference on Artificial Neural Networks, LNCS, vol. 6692, pp. 308–315. Springer (2011)

    Google Scholar 

  15. Fernández de Vega, F., Galeano, G., Gómez, J.A., and, J.M.S.: Efficient use of computational resources in genetic programming: Controlling the bloat phenomenon by means of the island model. In: Conference of the Industrial Electronics Society, vol. 3, pp. 2520–2524. IEEE (2002)

    Google Scholar 

  16. Fernández de Vega, F., Gil, G.G., Gómez Pulido, J.A., Guisado, J.L.: Control of bloat in genetic programming by means of the island model. In: Parallel Problem Solving from Nature-PPSN VIII, LNCS, vol. 3242, pp. 263–271. Springer (2004)

    Google Scholar 

  17. Whigham, P.A., Dick, G.: Implicitly controlling bloat in genetic programming. IEEE Transactions on Evolutionary Computation 14(2), 173–190 (2010)

    CrossRef  Google Scholar 

  18. White, D.R.: Software review: The ECJ toolkit. Genetic Programming and Evolvable Machines 13(1), 65–67 (2012)

    CrossRef  Google Scholar 

  19. Zaki, M.J., Li, W., Parthasarathy, S.: Customized dynamic load balancing for a network of workstations. Journal of Parallel and Distributed Computing (1997)

    Google Scholar 

Download references

Acknowledgements

We acknowledge support from Spanish Ministry of Economy and Competitiveness under project TIN2017-85727-C4-f2,4g-P, Regional Government of Extremadura, Department of Commerce and Economy, the European Regional Development Fund, a way to build Europe, under the project IB16035, Junta de Extremadura, project GR15068, and CICESE project 634-128.

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Authors and Affiliations

  1. University of Extremadura, Badajoz, Spain

    Francisco Fernández de Vega, Francisco Chávez & Daniel Lanza

  2. CICESE, Ensenada, BC, Mexico

    Gustavo Olague

  3. Beacon Center, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf & Erik Goodman

Authors
  1. Francisco Fernández de Vega
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  2. Gustavo Olague
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  3. Francisco Chávez
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  4. Daniel Lanza
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  5. Wolfgang Banzhaf
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  6. Erik Goodman
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Corresponding author

Correspondence to Francisco Fernández de Vega .

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

    Prof. Dr. Erik Goodman

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

    Leigh Sheneman

  4. Depto Ingenieria en Electronic Electrica Tecnológico Nacional de México/ IT, Tijuana, Baja California, Mexico

    Dr. Leonardo Trujillo

  5. Evolution Enterprises, Ann Arbor, MI, USA

    Bill Worzel

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de Vega, F.F., Olague, G., Chávez, F., Lanza, D., Banzhaf, W., Goodman, E. (2020). It Is Time for New Perspectives on How to Fight Bloat in GP. 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_2

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  • DOI: https://doi.org/10.1007/978-3-030-39958-0_2

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