Inclusiveness Target Country Conference Grant

Enhancing Algorithm Performance Prediction in Constrained Multiobjective Optimization Using Additional Training Problems

Andrejaana Andova, Jožef Stefan Institute, Ljubljana, Slovenia
Jordan N. Cork, Jožef Stefan Institute, Ljubljana, Slovenia
Tea Tušar, Jožef Stefan Institute, Ljubljana, Slovenia
Bogdan Filipič, Jožef Stefan Institute, Ljubljana, Slovenia

The Genetic and Evolutionary Computation Conference (GECCO 2024)
Melbourne, Australia
14-18 July 2024

Grantee: Andrejaana Andova, Jožef Stefan Institute, Ljubljana, Slovenia
Type of publication: Full paper
Type of presentation: Oral

Start date: 2024-07-13
End date: 2024-07-19
Awarded: 2024-06-19
Report approved: 2024-08-29

Abstract

A research problem studied extensively in recent years is the prediction of optimization algorithm performance. A common approach is using the landscape features of optimization problems to train machine learning models. These models are then used to predict algorithm performance. Due to the small number of constrained multiobjective optimization problems (CMOPs) available for benchmarking, training a machine learning model to predict algorithm performance is a hard task. To address this issue, this study uses the functions from the bbob and bbob-constrained benchmark problems to generate new CMOPs. These are then used as additional training examples for the machine learning models. Given the large number of generated CMOPs, the experiments in this study are limited to those with two objectives and two variables. The obtained results are promising. Using additional problems in the training phase improves the predictions in half of the defined classification tasks.

Publication

@inproceedings{Andova2024Enhancing,
	address = {Melbourne, Australia},
	author = {Andova, Andrejaana and Cork, Jordan N. and Tu{\v s}ar, Tea and Filipi{\v c}, Bogdan},
	booktitle = {GECCO \textquoteright{}24: Proceedings of the genetic and evolutionary computation conference},
	doi = {10.1145/3638529.3654098},
	year = {2024},
	month = {7},
	pages = {458--466},
	organization = {ACM},
	title = {Enhancing algorithm performance prediction in constrained multiobjective optimization using additional training problems},
}