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Neuroevolutionary multi-objective approaches to trajectory prediction in autonomous vehicles

Authors:

Fergal Stapleton,

Senthil YogamaniAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 675 - 678

https://doi.org/10.1145/3520304.3528984

Published: 19 July 2022 Publication History

Abstract

The incentive for using Evolutionary Algorithms (EAs) for the automated optimization and training of deep neural networks (DNNs), a process referred to as neuroevolution, has gained momentum in recent years. The configuration and training of these networks can be posed as optimization problems. Indeed, most of the recent works on neuroevolution have focused their attention on single-objective optimization. Moreover, from the little research that has been done at the intersection of neuroevolution and evolutionary multi-objective optimization (EMO), all the research that has been carried out has focused predominantly on the use of one type of DNN: convolutional neural networks (CNNs), using well-established standard benchmark problems such as MNIST. In this work, we make a leap in the understanding of these two areas (neuroevolution and EMO), regarded in this work as neuroevolutionary multi-objective, by using and studying a rich DNN composed of a CNN and Long-short Term Memory network. Moreover, we use a robust and challenging vehicle trajectory prediction problem. By using the well-known Non-dominated Sorting Genetic Algorithm-II, we study the effects of five different objectives, tested in categories of three, allowing us to show how these objectives have either a positive or detrimental effect in neuroevolution for trajectory prediction in autonomous vehicles.

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Cited By

Stapleton FCody-Kenny BGalván E(2025)NeuroLGP-SM: A Surrogate-Assisted Neuroevolution Approach Using Linear Genetic ProgrammingOptimization and Learning10.1007/978-3-031-77941-1_6(67-81)Online publication date: 27-Jan-2025
https://doi.org/10.1007/978-3-031-77941-1_6
Liang ZJiang TSun KCheng RLi XHandl J(2024)GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEAProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654223(566-575)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654223
Stapleton FGalvan E(2024)NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612039(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/CEC60901.2024.10612039
Show More Cited By

Index Terms

Neuroevolutionary multi-objective approaches to trajectory prediction in autonomous vehicles
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
      1. Motion path planning
  2. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches

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Published In

cover image ACM Conferences

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2022

2395 pages

ISBN:9781450392686

DOI:10.1145/3520304

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

Copyright © 2022 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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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: 19 July 2022

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Science Foundation Ireland

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GECCO '22

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SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

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

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Cited By

Stapleton FCody-Kenny BGalván E(2025)NeuroLGP-SM: A Surrogate-Assisted Neuroevolution Approach Using Linear Genetic ProgrammingOptimization and Learning10.1007/978-3-031-77941-1_6(67-81)Online publication date: 27-Jan-2025
https://doi.org/10.1007/978-3-031-77941-1_6
Liang ZJiang TSun KCheng RLi XHandl J(2024)GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEAProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654223(566-575)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654223
Stapleton FGalvan E(2024)NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612039(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/CEC60901.2024.10612039
Stapleton FGalvan ESilva SPaquete L(2023)Initial Steps Towards Tackling High-dimensional Surrogate Modeling for Neuroevolution Using Kriging Partial Least SquaresProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596437(83-84)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596437
Wang CJiao LZhao JLi LLiu XLiu FYang S(2023)Bi-Level Multiobjective Evolutionary Learning: A Case Study on Multitask Graph Neural Topology SearchIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.325526328:1(208-222)Online publication date: 10-Mar-2023
https://dl.acm.org/doi/10.1109/TEVC.2023.3255263
Galván EStapleton F(2023)Evolutionary Multi-objective Optimisation in Neurotrajectory PredictionApplied Soft Computing10.1016/j.asoc.2023.110693146:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110693

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