Bell, I.
(2019),
CEGO: C++11 Evolutionary Global Optimization, Journal of Open Source Software, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=926463
(Accessed September 13, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
CEGO: C++11 Evolutionary Global Optimization
Published
Author(s)
Abstract
Global optimization is an algorithmic need that is ubiquitous throughout the natural sciences, engineering, and other technical spheres. It is a non-trivial task, particularly when the function to be optimized has many local minima and the optimization algorithm may get trapped in the local minima of the function to be optimized. For that reason, many competing approaches have been proposed for the global optimization, especially nature-inspired global optimization approaches. The goal of the library proposed here is to develop a user-friendly framework in C++11 (with wrappers for Python) that can be used to successfully and efficiently carry out global optimization of challenging cost functions with a minimum of expertise required.Citation
Journal of Open Source Software
Pub Type
Journals
Download Paper
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created April 23, 2019, Updated January 2, 2020