Name
Abstract | ||
|---|---|---|
The interest in multimodal optimization methods is increasing in the last years. The objective is to find multiple solutions that allow the expert to choose the solution that better adapts to the actual conditions. Niching methods extend genetic algorithms to domains that require the identification of multiple solutions. There are different niching genetic algorithms: sharing, clearing, crowding and sequential, etc. The aim of this study is to study the applicability and the behavior of several niching genetic algorithms in solving job shop scheduling problems, by establishing a criterion in the use of different methods according to the needs of the expert. We will experiment with different instances of this problem, analyzing the behavior of the algorithms from the efficacy and diversity points of view. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1023/A:1024649709582 | J. Intelligent Manufacturing |
Keywords | Field | DocType |
Job shop scheduling problem,multimodal optimization,genetic algorithms,niching methods | Mathematical optimization,Job shop scheduling,Job shop scheduling problem,Crowding,Computer science,Flow shop scheduling,Artificial intelligence,Machine learning,Genetic algorithm | Journal |
Volume | Issue | ISSN |
14 | 3 | 1572-8145 |
Citations | PageRank | References |
17 | 0.74 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| E. Pérez | 1 | 41 | 1.78 |
| Francisco Herrera | 2 | 27391 | 1168.49 |
| C. Hernández | 3 | 17 | 0.74 |