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Extrema Graphs: Fitness Landscape Analysis to the Extreme!

Sophie Sadler, Alma Rahat Orcid Logo, David J. Walker, Daniel Archambault Orcid Logo

GECCO 2023: The Genetic and Evolutionary Computation Conference, Lisbon. July 15-19 2023.

Swansea University Authors: Sophie Sadler, Alma Rahat Orcid Logo, Daniel Archambault Orcid Logo

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DOI (Published version): 10.1145/3583133.3596343

Abstract

Fitness landscape analysis often relies on visual tools to provide insight to a search space, allowing for reasoning before optimisation. Currently, the dominant approach for visualisation is the local optima network, where the local structure around a potential global optimum is visualised using a...

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Published in: GECCO 2023: The Genetic and Evolutionary Computation Conference, Lisbon. July 15-19 2023.
ISBN: 9798400701207
Published: ACM (Association for Computing Machinery) 2023
URI: https://cronfa.swan.ac.uk/Record/cronfa63398
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spelling v2 63398 2023-05-11 Extrema Graphs: Fitness Landscape Analysis to the Extreme! 780d416ff624ef8e4541830674bfac0e Sophie Sadler Sophie Sadler true false 6206f027aca1e3a5ff6b8cd224248bc2 0000-0002-5023-1371 Alma Rahat Alma Rahat true false 8fa6987716a22304ef04d3c3d50ef266 0000-0003-4978-8479 Daniel Archambault Daniel Archambault true false 2023-05-11 MACS Fitness landscape analysis often relies on visual tools to provide insight to a search space, allowing for reasoning before optimisation. Currently, the dominant approach for visualisation is the local optima network, where the local structure around a potential global optimum is visualised using a network with the nodes as local minima and the edges as transitions between those minima through an optimiser. In this paper, we present an approach based on extrema graphs, originally used for isosurface extraction in volume visualisation, where transitions are captured between both maxima and minima embedded in two dimensions through dimensionality reduction techniques (multidimensional scaling in our prototype). These diagrams enable evolutionary computation practitioners to understand the entire search space by incorporating global information describing the spatial relationships between extrema. We demonstrate the approach on a number of continuous benchmark problems from the literature and highlight that the resulting visualisations enable the observation of known problem features, leading to the conclusion that extrema graphs are a suitable tool for extracting global information about problem landscapes. Conference Paper/Proceeding/Abstract GECCO 2023: The Genetic and Evolutionary Computation Conference, Lisbon. July 15-19 2023. ACM (Association for Computing Machinery) 9798400701207 24 7 2023 2023-07-24 10.1145/3583133.3596343 GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) Engineering and Physical Science Research Council [grant numbers EP/S023992/1 and EP/W01226X/1]. 2024-06-03T14:03:24.2795618 2023-05-11T09:29:43.0141670 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Sophie Sadler 1 Alma Rahat 0000-0002-5023-1371 2 David J. Walker 3 Daniel Archambault 0000-0003-4978-8479 4 63398__27572__24f5e77afd17436da5eb2aaa1c22df9b.pdf Fitness_Landscape_Analysis__GECCO_2023_ (1).pdf 2023-05-22T16:44:16.5259361 Output 9404493 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution International 4.0 License. Copyright © 2023 Owner/Author(s) true eng
title Extrema Graphs: Fitness Landscape Analysis to the Extreme!
spellingShingle Extrema Graphs: Fitness Landscape Analysis to the Extreme!
Sophie Sadler
Alma Rahat
Daniel Archambault
title_short Extrema Graphs: Fitness Landscape Analysis to the Extreme!
title_full Extrema Graphs: Fitness Landscape Analysis to the Extreme!
title_fullStr Extrema Graphs: Fitness Landscape Analysis to the Extreme!
title_full_unstemmed Extrema Graphs: Fitness Landscape Analysis to the Extreme!
title_sort Extrema Graphs: Fitness Landscape Analysis to the Extreme!
author_id_str_mv 780d416ff624ef8e4541830674bfac0e
6206f027aca1e3a5ff6b8cd224248bc2
8fa6987716a22304ef04d3c3d50ef266
author_id_fullname_str_mv 780d416ff624ef8e4541830674bfac0e_***_Sophie Sadler
6206f027aca1e3a5ff6b8cd224248bc2_***_Alma Rahat
8fa6987716a22304ef04d3c3d50ef266_***_Daniel Archambault
author Sophie Sadler
Alma Rahat
Daniel Archambault
author2 Sophie Sadler
Alma Rahat
David J. Walker
Daniel Archambault
format Conference Paper/Proceeding/Abstract
container_title GECCO 2023: The Genetic and Evolutionary Computation Conference, Lisbon. July 15-19 2023.
publishDate 2023
institution Swansea University
isbn 9798400701207
doi_str_mv 10.1145/3583133.3596343
publisher ACM (Association for Computing Machinery)
college_str Faculty of Science and Engineering
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department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Fitness landscape analysis often relies on visual tools to provide insight to a search space, allowing for reasoning before optimisation. Currently, the dominant approach for visualisation is the local optima network, where the local structure around a potential global optimum is visualised using a network with the nodes as local minima and the edges as transitions between those minima through an optimiser. In this paper, we present an approach based on extrema graphs, originally used for isosurface extraction in volume visualisation, where transitions are captured between both maxima and minima embedded in two dimensions through dimensionality reduction techniques (multidimensional scaling in our prototype). These diagrams enable evolutionary computation practitioners to understand the entire search space by incorporating global information describing the spatial relationships between extrema. We demonstrate the approach on a number of continuous benchmark problems from the literature and highlight that the resulting visualisations enable the observation of known problem features, leading to the conclusion that extrema graphs are a suitable tool for extracting global information about problem landscapes.
published_date 2023-07-24T14:03:23Z
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