Hybrid Acquisition Processes in Surrogate-Based Optimization. Application to Covid-19 Contact Reduction
10th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2022
; 13627 LNCS:127-141, 2022.
Article
in English
| Scopus | ID: covidwho-2148643
ABSTRACT
Parallel Surrogate-Assisted Evolutionary Algorithms (P-SAEAs) are based on surrogate-informed reproduction operators to propose new candidates to solve computationally expensive optimization problems. Differently, Parallel Surrogate-Driven Algorithms (P-SDAs) rely on the optimization of a surrogate-informed metric of promisingness to acquire new solutions. The former are promoted to deal with moderately computationally expensive problems while the latter are put forward on very costly problems. This paper investigates the design of hybrid strategies combining the acquisition processes of both P-SAEAs and P-SDAs to retain the best of both categories of methods. The objective is to reach robustness with respect to the computational budgets and parallel scalability. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Scopus
Language:
English
Journal:
10th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2022
Year:
2022
Document Type:
Article
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