Toggle Main Menu Toggle Search

Open Access padlockePrints

Extrema Graphs: Fitness Landscape Analysis to the Extreme!

Lookup NU author(s): Professor 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.

Publication metadata

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


DOI: 10.1145/3583133.3596343

Library holdings: Search Newcastle University Library for this item

ISBN: 9798400701207