@inproceedings{10.1145/3449639.3459297,
author = {Blank, Julian and Deb, Kalyanmoy},
title = {PSAF: A Probabilistic Surrogate-Assisted Framework for Single-Objective Optimization},
year = {2021},
isbn = {9781450383509},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3449639.3459297},
doi = {10.1145/3449639.3459297},
abstract = {In the last two decades, significant effort has been made to solve computationally
expensive optimization problems using surrogate models. Regardless of whether surrogates
are the primary drivers of an algorithm or improve the convergence of an existing
method, most proposed concepts are rather specific and not very generalizable. Some
important considerations are selecting a baseline optimization algorithm, a suitable
surrogate methodology, and the surrogate's involvement in the overall algorithm design.
This paper proposes a probabilistic surrogate-assisted framework (PSAF), demonstrating
its applicability to a broad category of single-objective optimization methods. The
framework injects knowledge from a surrogate into an existing algorithm through a
tournament-based procedure and continuing the optimization run on the surrogate's
predictions. The surrogate's involvement is determined by updating a replacement probability
based on the accuracy from past iterations. A study of four well-known population-based
optimization algorithms with and without the proposed probabilistic surrogate assistance
indicates its usefulness in achieving a better convergence. The proposed framework
enables the incorporation of surrogates into an existing optimization algorithm and,
thus, paves the way for new surrogate-assisted algorithms dealing with challenges
in less frequently addressed computationally expensive functions, such as different
variable types, large dimensional problems, multiple objectives, and constraints.},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
pages = {652–659},
numpages = {8},
keywords = {simulation optimization, metamodel-based optimization, surrogate-assisted optimization, genetic algorithms, evolutionary computing},
location = {Lille, France},
series = {GECCO '21}
}