DocumentCode
2917417
Title
Analysis and extension of the Inc* on the satisfiability testing problem
Author
Bader-El-Den, Mohamed ; Poli, Riccardo
Author_Institution
Dept. of Comput. & Electron. Syst., Univ. of Essex, Colchester
fYear
2008
fDate
1-6 June 2008
Firstpage
3342
Lastpage
3349
Abstract
Inc* is a general algorithm that can be used in conjunction with any local search heuristic and that has the potential to substantially improve the overall performance of the heuristic. The general idea of the algorithm is the following. Rather than attempting to directly solve a difficult problem, the algorithm dynamically chooses a smaller instance of the problem, and then increases the size of the instance only after the previous simplified instances have been solved, until the full size of the problem is reached. Genetic programming is used to discover new strategies for Inc*. Preliminary experiments on the satisfiability problem (SAT) problem have shown that Inc* is a competitive approach. In this paper we enhance Inc* and we experimentally test it on larger set of benchmarks, including big instances of SAT. Furthermore, we provide an analysis of the algorithmpsilas behaviour.
Keywords
computational complexity; genetic algorithms; search problems; Inc; genetic programming; local search heuristic; satisfiability testing problem; Algorithm design and analysis; Benchmark testing; Gain; Genetic programming; Heuristic algorithms; Labeling; Random variables; Traveling salesman problems;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-1822-0
Electronic_ISBN
978-1-4244-1823-7
Type
conf
DOI
10.1109/CEC.2008.4631250
Filename
4631250
Link To Document