Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30940
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Ochoa, Gabriela
Malan, Katherine M
Blum, Christian
Contact Email: gabriela.ochoa@stir.ac.uk
Title: Search Trajectory Networks of Population-based Algorithms in Continuous Spaces
Editor(s): Jiménez Laredo, Juan Luis
Fernández de Vega, Francisco
Castillo, Pedro A
Citation: Ochoa G, Malan KM & Blum C (2020) Search Trajectory Networks of Population-based Algorithms in Continuous Spaces. In: Jiménez Laredo JL, Fernández de Vega F & Castillo PA (eds.) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science, 12104. Applications of Evolutionary Computation – 23rd International Conference, EvoApplications 2020, Seville, Spain, 15.04.2020-17.04.2020. Cham, Switzerland: Springer, pp. 70-85. https://doi.org/10.1007/978-3-030-43722-0_5
Issue Date: 2020
Series/Report no.: Lecture Notes in Computer Science, 12104
Conference Name: Applications of Evolutionary Computation – 23rd International Conference, EvoApplications 2020
Conference Dates: 2020-04-15 - 2020-04-17
Conference Location: Seville, Spain
Abstract: We introduce search trajectory networks (STNs) as a tool to analyse and visualise the behaviour of population-based algorithms in continuous spaces. Inspired by local optima networks (LONs) that model the global structure of search spaces, STNs model the search tra-jectories of algorithms. Unlike LONs, the nodes of the network are not restricted to local optima but instead represent a given state of the search process. Edges represent search progression between consecutive states. This extends the power and applicability of network-based models to understand heuristic search algorithms. We extract and analyse STNs for two well-known population-based algorithms: particle swarm optimi-sation and differential evolution when applied to benchmark continuous optimisation problems. We also offer a comparative visual analysis of the search dynamics in terms of merged search trajectory networks.
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 article published in Castillo PA, Jiménez Laredo JL & Fernández de Vega F (eds.) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science, 12104. Applications of Evolutionary Computation – 23rd International Conference, EvoApplications 2020, Seville, Spain, 15.04.2020-17.04.2020. Cham, Switzerland: Springer, pp. 70-85. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-43722-0_5.

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