Abstract
The application of evolutionary algorithms in continuous optimization is a well-studied area of research. Nevertheless, recently there have been numerous works associated with surrogate-assisted approaches. This paper introduces LQ-R-SHADE: R-SHADE extended with a quadratic surrogate model. The principles of LQ-R-SHADE and its enhancements over the base R-SHADE are discussed in detail. The extension consists of the three main components: an archive of samples, a prescreening meta-model, and an initialization supported by the meta-model. In order to take advantage of the meta-model utilization as early as possible, a cascade of models is proposed: linear, quadratic, and quadratic with interactions. The proposed algorithm relies on multiple generation of mutated versions of each individual. The prescreening meta-model is then applied to select the most promising candidates for further evaluation with the use of a (costly) true fitness function. The performance of LQ-R-SHADE is evaluated on the well-known COCO BBOB benchmark and compared with the baseline R-SHADE method and its extension SHADE-LM, showing the advantage of the proposed algorithm. Besides numerical assessment, the impact of particular meta-model components on the obtained results is examined.
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Notes
- 1.
LQ-R-RSHADE is maintained as an open source project available at: https://bitbucket.org/mateuszzaborski/lqrshade/.
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Studies were funded by BIOTECHMED-1 project granted by Warsaw University of Technology under the program Excellence Initiative: Research University (ID-UB).
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Zaborski, M., Mańdziuk, J. (2023). LQ-R-SHADE: R-SHADE with Quadratic Surrogate Model. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13588. Springer, Cham. https://doi.org/10.1007/978-3-031-23492-7_23
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