Title
Evolutionary computing on consumer graphics hardware
Document Type
Journal article
Source Publication
IEEE Intelligent Systems
Publication Date
1-1-2007
Volume
22
Issue
2
First Page
69
Last Page
78
Abstract
Evolutionary algorithms (EA) are proven effective and robust in searching large varied spaces in a wide range of applications such as feature selection, electrical-circuit synthesis, and data mining. A growing demand from the multimedia and games industries has enabled graphics hardware companies to develop high-performance parallel graphics accelerators that resulted in the development of graphics processing unit (GPU). GPU handles rendering requests using a 3D graphics application programming interface (API). GPU lets processors communicate with any other processor directly, which enables to implement more flexible, fine grained EAs. A parallel EA can be implemented on consumer graphics cards found in many PCs. Evolutionary programming and genetic algorithms have been successfully applied to several numerical and optimization problems. EP requires mutation and is less computationally intensive than Genetic algorithm.
DOI
10.1109/MIS.2007.28
Print ISSN
15411672
E-ISSN
19411294
Publisher Statement
Copyright © 2007 Institute of Electrical and Electronics Engineers
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Fok, K.-L., Wong, T.-T., & Wong, M.-L. (2007). Evolutionary computing on consumer graphics hardware. IEEE Intelligent Systems, 22(2), 69-78. doi: 10.1109/MIS.2007.28
