Combining cellular genetic algorithms and local search for solving satisfiability problems

被引：16
|
作者：
Folino, G [1 ]
Pizzuti, C [1 ]
Spezzano, G [1 ]
机构：
[1] Univ Calabria, ISI, CNR, DEIS, I-87036 Arcavacata Di Rende, CS, Italy
来源：
TENTH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 1998年
关键词：
D O I：
10.1109/TAI.1998.744842
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 parallel computation model. The algorithm has been tested on randomly generated problems and some classes of problems from the DIMACS test set.
引用
收藏
页码：192 / 198
页数：7
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