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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dignum, S., Poli, R.: Operator equalisation and bloat free GP. In: European Conference on Genetic Programming, LNCS, vol. 4971, pp. 110–121. Springer (2008)
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)
Koza, J.R.: Genetic programming: On the programming of computers by means of natural selection. MIT Press (1992)
Langdon, W.B., Poli, R.: Fitness causes bloat, pp. 13–22. Soft Computing in Engineering Design and Manufacturing. Springer (1998)
Luke, S., Panait, L.: Lexicographic parsimony pressure. In: Conference on Genetic and Evolutionary Computation, pp. 829–836 (2002)
Luke, S., Panait, L.: A comparison of bloat control methods for genetic programming. Evolutionary Computation 14(3), 309–344 (2006)
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)
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)
Silva, S.: Reassembling operator equalisation: A secret revealed. ACM SIGEVOlution 5(3), 10–22 (2011)
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)
Trujillo, L., Munoz, L., Galván-López, E., Silva, S.: Neat genetic programming: Controlling bloat naturally. Information Sciences (2016)
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)
Fernández de Vega, F.: Distributed genetic programming models with application to logic synthesis on FPGAs. Ph.D. thesis, University of Extremadura (2001)
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)
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)
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)
Whigham, P.A., Dick, G.: Implicitly controlling bloat in genetic programming. IEEE Transactions on Evolutionary Computation 14(2), 173–190 (2010)
White, D.R.: Software review: The ECJ toolkit. Genetic Programming and Evolvable Machines 13(1), 65–67 (2012)
Zaki, M.J., Li, W., Parthasarathy, S.: Customized dynamic load balancing for a network of workstations. Journal of Parallel and Distributed Computing (1997)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-39958-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39957-3
Online ISBN: 978-3-030-39958-0
eBook Packages: Computer ScienceComputer Science (R0)