Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/34356
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Sarti, Stefano
Adair, Jason
Ochoa, Gabriela
Contact Email: goc@cs.stir.ac.uk
Title: Neuroevolution Trajectory Networks of the Behaviour Space
Editor(s): Jiménez Laredo, Juan Luis
Hidalgo, J Ignacio
Babaagba, Kehinde Oluwatoyin
Citation: Sarti S, Adair J & Ochoa G (2022) Neuroevolution Trajectory Networks of the Behaviour Space. In: Jiménez Laredo JL, Hidalgo JI & Babaagba KO (eds.) Applications of Evolutionary Computation. Lecture Notes in Computer Science, 13224. EvoApplications 2022, Madrid, Spain, 20.04.2022-22.04.2022. Cham, Switzerland: Springer International Publishing, pp. 685-703. https://doi.org/10.1007/978-3-031-02462-7_43
Issue Date: 2022
Date Deposited: 24-May-2022
Series/Report no.: Lecture Notes in Computer Science, 13224
Conference Name: EvoApplications 2022
Conference Dates: 2022-04-20 - 2022-04-22
Conference Location: Madrid, Spain
Abstract: A network-based modelling technique, search trajectory networks (STNs), has recently helped to understand the dynamics of neuroevolution algorithms such as NEAT. Modelling and visualising variants of NEAT made it possible to analyse the dynamics of search operators. Thus far, this analysis was applied directly to the NEAT genotype space composed of neural network topologies and weights. Here, we extend this work, by illuminating instead the behavioural space, which is available when the evolved neural networks control the behaviour of agents. Recent interest in behaviour characterisation highlights the need for divergent search strategies. Quality-diversity and Novelty search are examples of divergent search, but their dynamics are not yet well understood. In this article, we examine the idiosyncrasies of three neuroevolution variants: novelty, random and objective search operating as usual on the genotypic search space, but analysed in the behavioural space. Results show that novelty is a successful divergent search strategy. However, its abilities to produce diverse solutions are not always consistent. Our visual analysis highlights interesting relationships between topological complexity and behavioural diversity which may pave the way for new characterisations and search strategies.
Status: AM - Accepted Manuscript
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. This is a post-peer-review, pre-copyedit version of an paper published in Jiménez Laredo JL, Hidalgo JI & Babaagba KO (eds.) Applications of Evolutionary Computation. Lecture Notes in Computer Science, 13224. EvoApplications 2022, Madrid, Spain, 20.04.2022-22.04.2022. Cham, Switzerland: Springer International Publishing, pp. 685-703. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-02462-7_43.
Licence URL(s): https://storre.stir.ac.uk/STORREEndUserLicence.pdf

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