Name
Abstract | ||
|---|---|---|
In this paper, we address the automated product design problem by two distinct evolutionary approaches: genetic algorithms and evolutionary ontologies. Based on the mechanisms and internal representation of each algorithm, their capabilities are different, which means that the structure and complexity of the products differs. We provide detailed description of the evolutionary ontologies: crossover, mutation, repair and selection operators. Finally, both approaches are tested, benchmarked and compared in the case of power train design. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00500-016-2292-x | Soft Comput. |
Keywords | Field | DocType |
Evolutionary ontologies, Genetic algorithms, Relational crossover, Automated product design | Ontology (information science),Crossover,Computer science,Theoretical computer science,Genetic representation,Artificial intelligence,Product design,Computer-automated design,Evolutionary programming,Evolutionary music,Machine learning,Genetic algorithm | Journal |
Volume | Issue | ISSN |
20 | 11 | 1433-7479 |
Citations | PageRank | References |
1 | 0.35 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oliviu Matei | 1 | 43 | 11.15 |
| Diana Contras | 2 | 10 | 2.10 |
| Petrica C. Pop | 3 | 183 | 27.86 |
| Honoriu Valean | 4 | 17 | 8.28 |