Skip to content

RDLNetworkEcosystem

Sign in Get started

© 2026 Raw Data Library. All rights reserved.

About FAQ Privacy Terms Contact

A Framework for Self-Tuning Algorithms — Xin-she Yang (2021) | RDL Network

Home
Publications
A Framework for Self-Tuning Algorithms

Shared by

Middlesex University - London

A Framework for Self-Tuning Algorithms

In: A Framework for Self-Tuning Algorithms (Elsevier eBooks)

Chapter In A Book 2021 English

Authors

Xin-she Yang
Middlesex University - London

Abstract

1 min read

All nature-inspired algorithms have algorithm-dependent parameters. Tuning such parameters can be a challenging task. This chapter introduces the objective of parameter tuning and highlights the challenges in this area.

Discussion(0)

Sign in to like and join the discussion.

No comments yet. Be the first to comment.

Publication Info

DOI: 10.1016/b978-0-12-821986-7.00020-2
Year: 2021
Published: —
Language: English

Chapter In A Book Details

Pages: 197-205
Publisher: Elsevier eBooks
Book Title: A Framework for Self-Tuning Algorithms

Timeline

Created:June 19, 2026

Related publications

Chapter in a book2014

A Framework for Self-Tuning Algorithms

Chapter in a book2021

Analysis of Algorithms

Chapter in a book2014

Analysis of Firefly Algorithms and Automatic Parameter Tuning

Parameter tuning of the firefly algorithm by three tuning methods: Standard Monte Carlo, quasi-Monte Carlo and latin hypercube sampling methods

Geethu Joy, Christian Huyck, Xin-she Yang

Journal of Computational Science

Chapter in a book2020

Probability theory for analyzing nature-inspired algorithms

Xingshi He, Qinwei Fan, Xin-she Yang