STHV-Net: Hypervolume Approximation based on Set Transformer

Zhu，Han; Shang，Ke; Ishibuchi，Hisao

10.1145/3583131.3590378

2023-07-15

Genetic and Evolutionary Computation Conference (GECCO)

GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference

804-812

JUL 15-19, 2023

null,Lisbon,PORTUGAL

1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES

ASSOC COMPUTING MACHINERY

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.

evolutionary multi-objective optimization HV-Net hypervolume approximation set transformer

第一 ; 通讯

英语

CPCI-S ; EI

National Natural Science Foundation of China["62002152","62250710163","62250710682"]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems

WOS:001031455100090

20233314553834

Errors ; Evolutionary algorithms ; Multiobjective optimization

Optimization Techniques:921.5 ; Mathematical Statistics:922.2

2-s2.0-85167725214

Scopus

Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China

Zhu，Han,Shang，Ke,Ishibuchi，Hisao. STHV-Net: Hypervolume Approximation based on Set Transformer[C]. 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES:ASSOC COMPUTING MACHINERY,2023:804-812.