TY - GEN
T1 - Surrogate-assisted Multi-objective Optimization via Genetic Programming Based Symbolic Regression
AU - Yang, Kaifeng
AU - Affenzeller, Michael
N1 - Funding Information:
Acknowledgments. This work is supported by the Austrian Science Fund (FWF – Der Wissenschaftsfonds) under the project (I 5315, ‘ML Methods for Feature Identification Global Optimization).
Publisher Copyright:
© 2023, The Author(s).
PY - 2023
Y1 - 2023
N2 - Surrogate-assisted optimization algorithms are a commonly used technique to solve expensive-evaluation problems, in which a regression model is built to replace an expensive function. In some acquisition functions, the only requirement for a regression model is the predictions. However, some other acquisition functions also require a regression model to estimate the “uncertainty” of the prediction, instead of merely providing predictions. Unfortunately, very few statistical modeling techniques can achieve this, such as Kriging/Gaussian processes, and recently proposed genetic programming-based (GP-based) symbolic regression with Kriging (GP2). Another method is to use a bootstrapping technique in GP-based symbolic regression to estimate prediction and its corresponding uncertainty. This paper proposes to use GP-based symbolic regression and its variants to solve multi-objective optimization problems (MOPs), which are under the framework of a surrogate-assisted multi-objective optimization algorithm (SMOA). Kriging and random forest are also compared with GP-based symbolic regression and GP2. Experiment results demonstrate that the surrogate models using the GP2 strategy can improve SMOA’s performance.
AB - Surrogate-assisted optimization algorithms are a commonly used technique to solve expensive-evaluation problems, in which a regression model is built to replace an expensive function. In some acquisition functions, the only requirement for a regression model is the predictions. However, some other acquisition functions also require a regression model to estimate the “uncertainty” of the prediction, instead of merely providing predictions. Unfortunately, very few statistical modeling techniques can achieve this, such as Kriging/Gaussian processes, and recently proposed genetic programming-based (GP-based) symbolic regression with Kriging (GP2). Another method is to use a bootstrapping technique in GP-based symbolic regression to estimate prediction and its corresponding uncertainty. This paper proposes to use GP-based symbolic regression and its variants to solve multi-objective optimization problems (MOPs), which are under the framework of a surrogate-assisted multi-objective optimization algorithm (SMOA). Kriging and random forest are also compared with GP-based symbolic regression and GP2. Experiment results demonstrate that the surrogate models using the GP2 strategy can improve SMOA’s performance.
KW - Genetic programming
KW - Multi-objective optimization
KW - Surrogate model
KW - Symbolic regression
UR - http://www.scopus.com/inward/record.url?scp=85151052993&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-27250-9_13
DO - 10.1007/978-3-031-27250-9_13
M3 - Conference contribution
AN - SCOPUS:85151052993
SN - 9783031272493
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 176
EP - 190
BT - Evolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Proceedings
A2 - Emmerich, Michael
A2 - Deutz, André
A2 - Wang, Hao
A2 - Kononova, Anna V.
A2 - Naujoks, Boris
A2 - Li, Ke
A2 - Miettinen, Kaisa
A2 - Yevseyeva, Iryna
PB - Springer
T2 - 12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023
Y2 - 20 March 2023 through 24 March 2023
ER -