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European Conference on the Applications of Evolutionary Computation

EvoApplications 2016: Applications of Evolutionary Computation pp 280–295Cite as

Genetic Programming Algorithms for Dynamic Environments

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9598))

Abstract

Evolutionary algorithms are a family of stochastic search heuristics that include Genetic Algorithms (GA) and Genetic Programming (GP). Both GAs and GPs have been successful in many applications, mainly with static scenarios. However, many real world applications involve dynamic environments (DE). Many work has been made to adapt GAs to DEs, but only a few efforts in adapting GPs for this kind of environments. In this paper we present novel GP algorithms for dynamic environments and study their performance using three dynamic benchmark problems, from the areas of Symbolic Regression, Classification and Path Planning. Furthermore, we apply the best algorithm we found in the navigation of an Erratic Robot through a dynamic Santa Fe Ant Trail and compare its performance to the standard GP algorithm. The results, statistically validated, are very promising.

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Acknowledgements

This work was partially supported by the TIRAMISU project (www.fp7-tiramisu.eu) under grant agreement FP7/SEC/284747 and the MassGP project (www.novaims.unl.pt/massgp) under grant agreement PTDC/EEI-CTP/2975/2012.

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Authors and Affiliations

  1. Institute of Systems and Robotics, Coimbra, Portugal

    João Macedo & Lino Marques

  2. Center of Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal

    Ernesto Costa

Authors
  1. João Macedo
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  2. Ernesto Costa
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  3. Lino Marques
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Corresponding author

Correspondence to João Macedo .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

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Macedo, J., Costa, E., Marques, L. (2016). Genetic Programming Algorithms for Dynamic Environments. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9598. Springer, Cham. https://doi.org/10.1007/978-3-319-31153-1_19

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  • DOI: https://doi.org/10.1007/978-3-319-31153-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31152-4

  • Online ISBN: 978-3-319-31153-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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