Swarm and stochastic computing for global optimization
Book chapter
Yang, X. 2021. Swarm and stochastic computing for global optimization. in: Adamatzky, A. (ed.) Handbook of Unconventional Computing - Volume 1: Theory World Scientific. pp. 469-487
Chapter title | Swarm and stochastic computing for global optimization |
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Authors | Yang, X. |
Abstract | Many problems in data mining and machine learning are related to optimization, and optimization techniques are often used to solve such problems. Traditional techniques such as gradient-based methods can be efficient, but they are local optimizers. For global optimization, alternative approaches tend to be nature-inspired metaheuristic algorithms. We introduce some of the nature-inspired optimization algorithms with the emphasis on their main characteristics. We also highlight the role of algorithmic components in such algorithms, and then we conclude with a brief discussion about some open problems. |
Page range | 469-487 |
Book title | Handbook of Unconventional Computing - Volume 1: Theory |
Editors | Adamatzky, A. |
Publisher | World Scientific |
Series | WSPC Book Series in Unconventional Computing |
ISBN | |
Hardcover | 9789811235719 |
Electronic | 9789811235726 |
Publication dates | |
Online | 22 Aug 2021 |
31 Oct 2021 | |
Publication process dates | |
Deposited | 02 Sep 2021 |
Output status | Published |
Digital Object Identifier (DOI) | https://doi.org/10.1142/9789811235726_0015 |
Language | English |
Journal | Handbook of Unconventional Computing |
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