Name
Abstract | ||
|---|---|---|
Constraint satisfaction problems (CSPs) widely occur in artificial intelligence. In the last twenty years, many algorithms and heuristics were developed to solve CSP. Recently, bio-inspired algorithms have been proposed to solve CSP. They have shown to be more efficient than systematic approaches in solving hard instances. Given that recent publications indicate that Immune systems offer advantages to solve complex problems, our aim here is to propose an efficient immune system which can solve CSPs. We propose an immune system which is able to solve hard constraint satisfaction problems. The tests were carried out using random generated binary constraint satisfaction problems on the transition phase. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CEC.2007.4424831 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
artificial immune systems,constraint theory,problem solving,artificial intelligence,bio-inspired algorithms,complex problems,immune system,random generated binary constraint satisfaction problems | Constraint satisfaction,Mathematical optimization,Artificial immune system,Computer science,Constraint theory,Constraint satisfaction problem,Constraint satisfaction dual problem,Heuristics,Artificial intelligence,Machine learning,Binary constraint,Complex problems | Conference |
ISBN | Citations | PageRank |
978-1-4244-1340-9 | 3 | 0.41 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| María Cristina Riff | 1 | 200 | 23.91 |
| Marcos Zuniga | 2 | 3 | 0.41 |