Title
Hybridizing Evolutionary Negative Selection Algorithm and Local Search for Large-Scale Satisfiability Problems
Abstract
This paper introduces a hybrid algorithm called as the HENSA-SAT for the large-scale Satisfiability (SAT) problems. The HENSA-SAT is the hybrid of Evolutionary Negative Selection Algorithm (ENSA), the Flip Heuristic, the BackForwardFlipHeuristic procedure and the VerticalClimbing procedure. The Negative Selection (NS) is called twice for different purposes. One is used to make the search start in as many different areas as possible. The other is used to restrict the times of calling the BackForwardFlipHeuristic for local search. The Flip Heuristic, the BackForwardFlipHeuristic procedure and the VerticalClimbing procedure are used to enhance the local search. Experiment results show that the proposed algorithm is competitive with the GASAT that is the state-of-the-art algorithm for the large-scale SAT problems.
Year
DOI
Venue
2009
10.1007/978-3-642-04843-2_27
ISICA
Keywords
Field
DocType
evolutionary negative selection,hybridizing evolutionary negative selection,hybrid algorithm,large-scale satisfiability problems,backforwardflipheuristic procedure,negative selection,local search,flip heuristic,state-of-the-art algorithm,search start,verticalclimbing procedure,proposed algorithm,sat,satisfiability
Mathematical optimization,Heuristic,Hybrid algorithm,Negative selection,Satisfiability,Algorithm,Negative selection algorithm,Local search (optimization),Best-first search,restrict,Mathematics
Conference
Volume
ISSN
Citations 
5821
0302-9743
0
PageRank 
References 
Authors
0.34
19
5
Name
Order
Citations
PageRank
Peng Guo100.34
Wenjian Luo235640.95
Zhifang Li3152.45
Houjun Liang4342.59
Xufa Wang560839.62