A New Scheme of Using Inference Inside Evolutionary Computation Techniques to Solve CSPs

Author

Ionita, Madalina ; Breaban, Mihaela ; Croitoru, Cornelius

Author_Institution

Fac. of Comput. Sci., "Al. I. Cuza" Univ., Iasi

fYear

2006

fDate

Sept. 2006

Firstpage

323

Lastpage

329

Abstract

Combining inference and search produces successful schemes for solving constraint satisfaction problems. Based on this idea a general scheme which uses inference inside evolutionary computation techniques is presented. A genetic algorithm and the particle swarm optimization heuristic make use of adaptable inference levels offered by the mini-bucket elimination algorithm. Experimental results prove the efficiency of our approach in solving the Max-CSP optimization task. The inference/search trade-off is analyzed

Keywords

constraint theory; genetic algorithms; inference mechanisms; particle swarm optimisation; constraint satisfaction problem; evolutionary computation; genetic algorithm; inference/search trade-off; minibucket elimination; particle swarm optimization; Algorithm design and analysis; Computer science; Constraint optimization; Design methodology; Design optimization; Evolutionary computation; Genetic algorithms; Inference algorithms; Particle swarm optimization; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Symbolic and Numeric Algorithms for Scientific Computing, 2006. SYNASC '06. Eighth International Symposium on

Conference_Location

Timisoara

Print_ISBN

0-7695-2740-X

Type

conf

DOI

10.1109/SYNASC.2006.7

Filename

4090337