Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework

General Purpose computing over Graphical Processing Units (GPGPUs) is a huge shift of paradigm in parallel computing that promises a dramatic increase in performance. But GPGPUs also bring an unprecedented level of complexity in algorithmic design and software development. In this paper we describe...

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Bibliographic Details
Published in:	Genetic programming and evolvable machines Vol. 10; no. 4; pp. 391 - 415
Main Authors:	Munawar, Asim, Wahib, Mohamed, Munetomo, Masaharu, Akama, Kiyoshi
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.12.2009
Subjects:	Algorithms Architecture Artificial Intelligence Biomedical Engineering and Bioengineering Compilers Computation Computer Science Electrical Engineering Genetic algorithms Genetics Interpreters Operators Original Paper Parallel processing Programming Languages Programming Techniques Searching Software Engineering/Programming and Operating Systems Single instruction multiple threads (SIMT) Genetic algorithm (GA) General-purpose computing on graphics processing unit (GPGPU) Single instruction multiple data (SIMD) Compute unified device architecture (CUDA) MAXimum SATisfiability problem (MAX-SAT)
ISSN:	1389-2576, 1573-7632
Online Access:	Get full text
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