Abstract
This Paper Offers a Parallel Genetic Algorithm Solution to the Satisfiability Problem. It Combines Components of the Davis-Putnam Method and Genetic Algorithms for the Solution. This Solution is Useful in the Areas of Theorem Proving, Constraint Satisfaction Programming, and VLSI Design. the Algorithm is Implemented and Run on a Paragon. the Results Show Performance Improvement by Increasing the Number of Nodes. Two Parallel Methods Are Compared: One that Implements Interprocessor Communication and One that Does Not. the Results Show Performance Improvement with the Method that Uses Interprocessor Communication.
Recommended Citation
N. Nemer-Preece and R. W. Wilkerson, "Parallel Genetic Algorithm to Solve the Satisfiability Problem," Proceedings of the ACM Symposium on Applied Computing, pp. 23 - 28, Association for Computing Machinery, Feb 1998.
The definitive version is available at https://doi.org/10.1145/330560.330565
Department(s)
Computer Science
Keywords and Phrases
David-Putnam method; Genetic algorithms; Model generation; Parallel processing; Satisfiability problem
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Association for Computing Machinery, All rights reserved.
Publication Date
27 Feb 1998
