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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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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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Correspondence to Francisco Fernández de Vega .

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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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  • Publisher Name: Springer, Cham

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