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Lookup NU author(s): Dr Daniel ArchambaultORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 Copyright held by the owner/author(s). 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.
Author(s): Sadler S, Walker DJ, Rahat A, Archambault D
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: GECCO 2023 Companion: Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
Year of Conference: 2023
Pages: 2081-2089
Online publication date: 24/07/2023
Acceptance date: 02/04/2018
Date deposited: 15/09/2023
Publisher: ACM
URL: https://doi.org/10.1145/3583133.3596343
DOI: 10.1145/3583133.3596343
Library holdings: Search Newcastle University Library for this item
ISBN: 9798400701207