Szczegóły publikacji
Opis bibliograficzny
Configuring a hierarchical evolutionary strategy using exploratory landscape analysis / Hubert GUZOWSKI, Maciej SMOŁKA // W: GECCO'23 [Dokument elektroniczny] : Genetic and Evolutionary Computation Conference Companion : 15–19 July 2023, Lisbon, Portugal : proceedings. — Wersja do Windows. — Dane tekstowe. — USA : Association for Computing Machinery, cop. 2023. — e-ISBN: 979-8-4007-0120-7. — S. 1785–1792. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://dl.acm.org/doi/pdf/10.1145/3583133 [2023-08-07]. — Bibliogr. s. 1790, 1792, Abstr.
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 148052 |
|---|---|
| Data dodania do BaDAP | 2023-09-04 |
| DOI | 10.1145/3583133.3596403 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | Genetic and Evolutionary Computations 2023 |
Abstract
Hierarchic Memetic Strategy (HMS) is a stochastic global optimizer designed to tackle highly multimodal problems. It consists of parallel running optimization methods organized in a tree hierarchy. Depending on the task, different algorithms can be utilized on each of the levels. In this paper, we incorporate into HMS's structure a mechanism for choosing its configuration based on information gathered by a set of Exploratory Landscape Analysis (ELA) methods and hyperparametric optimization. We compared the performance of such configured HMS with a portfolio of proven state-of-the-art algorithms on the suite of black-box optimization functions. The results of this work show the efficacy of HMS and provide a set of default parameters evaluated for algorithms users. The use of ELA methods to select the configuration of a composite algorithm extends their standard use as part of an algorithm selector and provides insight into the relationship between exploration and exploitation for different types of fitness functions.
