Details
| Original language | English |
|---|---|
| Title of host publication | GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion |
| Publication status | Accepted/In press - 2023 |
Abstract
with unknown problem structures. Bayesian Optimization (BO) is
a popular, surrogate-assisted and thus sample-efficient approach
for this setting. The BO pipeline itself is highly configurable with
many different design choices regarding the initial design, surrogate
model and acquisition function (AF). Unfortunately, our understand-
ing of how to select suitable components for a problem at hand is
very limited. In this work, we focus on the choice of the AF, whose
main purpose it is to balance the trade-off between exploring re-
gions with high uncertainty and those with high promise for good
solutions. We propose Self-Adjusting Weighted Expected Improve-
ment (SAWEI), where we let the exploration-exploitation trade-off
self-adjust in a data-driven manner based on a convergence crite-
rion for BO. On the BBOB functions of the COCO benchmark, our
method performs favorably compared to handcrafted baselines and
serves as a robust default choice for any problem structure. With
SAWEI, we are a step closer to on-the-fly, data-driven and robust
BO designs that automatically adjust their sampling behavior to
the problem at hand.
Keywords
- Bayesian optimization, Weighted Expected Improvement, Self-adjusting, Acquisition function schedules, Upper bound regret
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GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2023.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
AU - Benjamins, Carolin
AU - Raponi, Elena
AU - Jankovic, Anja
AU - Doerr, Carola
AU - Lindauer, Marius
PY - 2023
Y1 - 2023
N2 - In optimization, we often encounter expensive black-box problemswith unknown problem structures. Bayesian Optimization (BO) isa popular, surrogate-assisted and thus sample-efficient approachfor this setting. The BO pipeline itself is highly configurable withmany different design choices regarding the initial design, surrogatemodel and acquisition function (AF). Unfortunately, our understand-ing of how to select suitable components for a problem at hand isvery limited. In this work, we focus on the choice of the AF, whosemain purpose it is to balance the trade-off between exploring re-gions with high uncertainty and those with high promise for goodsolutions. We propose Self-Adjusting Weighted Expected Improve-ment (SAWEI), where we let the exploration-exploitation trade-offself-adjust in a data-driven manner based on a convergence crite-rion for BO. On the BBOB functions of the COCO benchmark, ourmethod performs favorably compared to handcrafted baselines andserves as a robust default choice for any problem structure. WithSAWEI, we are a step closer to on-the-fly, data-driven and robustBO designs that automatically adjust their sampling behavior tothe problem at hand.
AB - In optimization, we often encounter expensive black-box problemswith unknown problem structures. Bayesian Optimization (BO) isa popular, surrogate-assisted and thus sample-efficient approachfor this setting. The BO pipeline itself is highly configurable withmany different design choices regarding the initial design, surrogatemodel and acquisition function (AF). Unfortunately, our understand-ing of how to select suitable components for a problem at hand isvery limited. In this work, we focus on the choice of the AF, whosemain purpose it is to balance the trade-off between exploring re-gions with high uncertainty and those with high promise for goodsolutions. We propose Self-Adjusting Weighted Expected Improve-ment (SAWEI), where we let the exploration-exploitation trade-offself-adjust in a data-driven manner based on a convergence crite-rion for BO. On the BBOB functions of the COCO benchmark, ourmethod performs favorably compared to handcrafted baselines andserves as a robust default choice for any problem structure. WithSAWEI, we are a step closer to on-the-fly, data-driven and robustBO designs that automatically adjust their sampling behavior tothe problem at hand.
KW - Bayesian optimization
KW - Weighted Expected Improvement
KW - Self-adjusting
KW - Acquisition function schedules
KW - Upper bound regret
M3 - Conference contribution
BT - GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion
ER -