Authors:
André Thomaser
1
;
2
;
Marc-Eric Vogt
1
;
Thomas Bäck
2
and
Anna Kononova
2
Affiliations:
1
BMW Group, Knorrstraße 147, Munich, Germany
;
2
LIACS, Leiden University, Niels Bohrweg 1, Leiden, The Netherlands
Keyword(s):
Parameter Tuning, CMA-ES, Vehicle Dynamics Design, Benchmarking, Exploratory Landscape Analysis, Artificial Benchmarking Functions.
Abstract:
The algorithm selection problem is of paramount importance in achieving high-quality results while minimizing computational effort, especially when dealing with expensive black-box optimization problems. In this paper, we address this challenge by using randomly generated artificial functions that mimic the landscape characteristics of the original problem while being inexpensive to evaluate. The similarity between the artificial function and the original problem is quantified using Exploratory Landscape Analysis. We demonstrate a significant performance improvement on five real-world vehicle dynamics problems by transferring the parameters of the Covariance Matrix Adaptation Evolution Strategy tuned to these artificial functions. We provide a complete set of simulated values of braking distance for fully enumerated 2D design spaces of all five real-world optimization problems. So, replication of our results and benchmarking directly on the real-world problems is possible. Beyond the
scope of this paper, this data can be used as a benchmarking set for multi-objective optimization with up to five objectives.
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