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International Conference on the Applications of Evolutionary Computation (Part of EvoStar)

EvoApplications 2020: Applications of Evolutionary Computation pp 212–227Cite as

Locating Odour Sources with Geometric Syntactic Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12104))

Abstract

Using robots to locate odour sources is an interesting problem with important applications. Many researchers have drawn inspiration from nature to produce robotic methods, whilst others have attempted to automatically create search strategies with Artificial Intelligence techniques. This paper extends Geometric Syntactic Genetic Programming and applies it to automatically produce robotic controllers in the form of behaviour trees. The modification proposed enables Geometric Syntactic Genetic Programming to evolve trees containing multiple symbols per node. The behaviour trees produced by this algorithm are compared to those evolved by a standard Genetic Programming algorithm and to two bio-inspired strategies from the literature, both in simulation and in the real world. The statistically validated results show that the Geometric Syntactic Genetic Programming algorithm is able to produce behaviour trees that outperform the bio-inspired strategies, while being significantly smaller than those evolved by the standard Genetic Programming algorithm. Moreover, that reduction in size does not imply statistically significant differences in the performance of the strategies.

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References

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Acknowledgement

J. Macedo acknowledges the Portuguese Foundation for Science and Technology (FCT) for Ph.D. studentship SFRH/BD/129673/2017. This work was supported by national funds of FCT/MCTES under projects UID/EEA/00048/2019 and UID/CEC/00326/2019, and it is based upon work from COST Action CA15140: Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO).

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Authors and Affiliations

  1. ISR, Department of Electrical and Computer Engineering, University of Coimbra, 3030 290, Coimbra, Portugal

    João Macedo & Lino Marques

  2. CISUC, Department of Informatics Engineering, University of Coimbra, 3030 290, Coimbra, Portugal

    João Macedo & Ernesto Costa

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

Correspondence to João Macedo .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

  3. Universidad de Extremadura, Mérida, Spain

    Francisco Fernández de Vega

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Macedo, J., Marques, L., Costa, E. (2020). Locating Odour Sources with Geometric Syntactic Genetic Programming. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science(), vol 12104. Springer, Cham. https://doi.org/10.1007/978-3-030-43722-0_14

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  • DOI: https://doi.org/10.1007/978-3-030-43722-0_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43721-3

  • Online ISBN: 978-3-030-43722-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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