research-article

STHV-Net: Hypervolume Approximation based on Set Transformer

Authors:

Hisao IshibuchiAuthors Info & Claims

GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 804 - 812

https://doi.org/10.1145/3583131.3590378

Published: 12 July 2023 Publication History

Abstract

In this paper, we propose STHV-Net to approximate the hyper-volume indicator based on Set Transformer. Set Transformer is an advanced model to process set-form data which concentrates on the interaction of set elements. STHV-Net receives a non-dominated positive solution set of any size and outputs an approximate hyper-volume value of this solution set. The output value is independent of the order of the elements in the input set. The performance of STHV-Net is compared with three existing approximation methods (Monte Carlo, R2 indicator, HV-Net) using two evaluation criteria: approximation errors and computing time. Our experimental results show that STHV-Net is superior to the Monte Carlo method and the R2 indicator method with respect to these two criteria. Compared with HV-Net, our method can obtain lower approximation errors at the cost of a slightly longer computing time. We provide six representative models with different parameter sizes for users who have different preferences about the tradeoff between approximation error and computing time.

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Cited By

Wu GNan YShang KIshibuchi H(2024)GHVC-Net: Hypervolume Contribution Approximation Based on Graph Neural Network2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831090(5339-5346)Online publication date: 6-Oct-2024
https://doi.org/10.1109/SMC54092.2024.10831090

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STHV-Net: Hypervolume Approximation based on Set Transformer

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cover image ACM Conferences

GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference

July 2023

1667 pages

ISBN:9798400701191

DOI:10.1145/3583131

Chair:
Sara Silva,
Program Chair:
Luís Paquete

Copyright © 2023 ACM.

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Published: 12 July 2023

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GECCO '23: Genetic and Evolutionary Computation Conference

July 15 - 19, 2023

Lisbon, Portugal

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

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Cited By

Wu GNan YShang KIshibuchi H(2024)GHVC-Net: Hypervolume Contribution Approximation Based on Graph Neural Network2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831090(5339-5346)Online publication date: 6-Oct-2024
https://doi.org/10.1109/SMC54092.2024.10831090

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