research-article

An analysis of dimensionality reduction techniques for visualizing evolution

Authors:

Andrea De Lorenzo,

Alberto BartoliAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1864 - 1872

https://doi.org/10.1145/3319619.3326868

Published: 13 July 2019 Publication History

Abstract

We consider the problem of visualizing the population dynamics along an evolutionary run using a dimensionality reduction technique for mapping individuals from the original search space to a 2-D space. We quantitatively assess four of these techniques in terms of their ability to preserve useful information about (a) population movements and (b) exploration-exploitation trade-off. We propose two compact visualizations aimed at highlighting these two aspects of population dynamics and evaluate them qualitatively. The results are very promising as the proposed framework is indeed able to represent crucial properties of population dynamics in a way that is both highly informative and simple to understand.

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Cited By

Chacón Sartori CBlum COchoa GLi XHandl J(2024)An Extension of STNWeb Functionality: On the Use of Hierarchical Agglomerative Clustering as an Advanced Search Space Partitioning StrategyProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654084(151-159)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654084
Tran BLuong NLi XHandl J(2024)On the Investigation of Multimodal Evolutionary Algorithms Using Search Trajectory NetworksProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654050(32-40)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654050
Zhang ZYang FCheng RMa Y(2024)ParetoTracker: Understanding Population Dynamics in Multi-Objective Evolutionary Algorithms Through Visual AnalyticsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345614231:1(820-830)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456142
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An analysis of dimensionality reduction techniques for visualizing evolution

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cover image ACM Conferences

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

2161 pages

ISBN:9781450367486

DOI:10.1145/3319619

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Copyright © 2019 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

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Slovenian Research Agency
AWS Cloud Credits for Research

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GECCO '19

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SIGEVO

GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Chacón Sartori CBlum COchoa GLi XHandl J(2024)An Extension of STNWeb Functionality: On the Use of Hierarchical Agglomerative Clustering as an Advanced Search Space Partitioning StrategyProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654084(151-159)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654084
Tran BLuong NLi XHandl J(2024)On the Investigation of Multimodal Evolutionary Algorithms Using Search Trajectory NetworksProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654050(32-40)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654050
Zhang ZYang FCheng RMa Y(2024)ParetoTracker: Understanding Population Dynamics in Multi-Objective Evolutionary Algorithms Through Visual AnalyticsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345614231:1(820-830)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456142
Nikitin NPinchuk MPokrovskii VShevchenko PGetmanov AAksenkin YRevin IStebenkov ALatypov VPoslavskaya EKalyuzhnaya A(2024)Integration of evolutionary automated machine learning with structural sensitivity analysis for composite pipelinesKnowledge-Based Systems10.1016/j.knosys.2024.112363302(112363)Online publication date: Oct-2024
https://doi.org/10.1016/j.knosys.2024.112363
Ji LQiu SXu ZLiu YYang G(2024)Comparing dimensionality reduction techniques for visual analysis of the LSTM hidden activity on multi-dimensional time series modelingThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-03235-940:11(8243-8261)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s00371-023-03235-9
Chacon-Sartori CBlum COchoa G(2023)Search Trajectory Networks Meet the Web: A Web Application for the Visual Comparison of Optimization AlgorithmsProceedings of the 2023 12th International Conference on Software and Computer Applications10.1145/3587828.3587843(89-96)Online publication date: 23-Feb-2023
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Benecke TMostaghim S(2023)Effects of Optimal Genetic Material in the Initial Population of Evolutionary Algorithms2023 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI52147.2023.10372037(1386-1391)Online publication date: 5-Dec-2023
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Janssen DPullan WLiew A(2023)Evolutionary Computation Visualization: ECvisIEEE Access10.1109/ACCESS.2023.324407811(16474-16482)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3244078
Nolle LStahl FEl-Mihoub T(2023)On Explanations for Hybrid Artificial IntelligenceArtificial Intelligence XL10.1007/978-3-031-47994-6_1(3-15)Online publication date: 8-Nov-2023
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Walter MWalker DCraven MWagner M(2022)An explainable visualisation of the evolutionary search processProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533984(1794-1802)Online publication date: 9-Jul-2022
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