Combining cellular genetic algorithms and local search for solving satisfiability problems

Author

Folino, Gianluigi ; Pizzuti, Clara ; Spezzano, Giandomenico

Author_Institution

ISI, Calabria Univ., Italy

fYear

1998

fDate

10-12 Nov 1998

Firstpage

192

Lastpage

198

Abstract

A new parallel hybrid method for solving the satisfiability problem that combines cellular genetic algorithms and the random walk (WSAT) strategy of GSAT is presented. The method, called CGWSAT, uses a cellular genetic algorithm to perform a global search on a random initial population of candidate solutions and a local selective generation of new strings. Global search is specialized in local search by adopting the WSAT strategy. CGWSAT has been implemented on a Meiko CS-2 parallel machine using a two-dimensional cellular automaton as a parallel computation model. The algorithm has been tested on randomly generated problems and some classes of problems from the DIMACS test set

Keywords

cellular automata; computability; genetic algorithms; parallel algorithms; parallel machines; problem solving; search problems; 2D cellular automaton; CGWSAT; DIMACS test set; GSAT; Meiko CS-2 parallel machine; cellular genetic algorithms; global search; local search; parallel computation model; parallel hybrid method; random walk; satisfiability problem solving; strings; two-dimensional cellular automaton; Artificial intelligence; Automata; Computational modeling; Computer vision; Concurrent computing; Genetic algorithms; Logic design; Parallel machines; Search methods; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 1998. Proceedings. Tenth IEEE International Conference on

Conference_Location

Taipei

ISSN

1082-3409

Print_ISBN

0-7803-5214-9

Type

conf

DOI

10.1109/TAI.1998.744842

Filename

744842