Name
Title | ||
|---|---|---|
Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models |
Abstract | ||
|---|---|---|
In this paper we describe a method for improving genetic-algorithm-based optimiza- tion using informed genetic operators. The idea is to make the genetic operators such as mutation and crossover more informed us- ing reduced models. In every place where a random choice is made, for example when a point is mutated, instead of generating just one random mutation we generate several, rank them using a reduced model, then take the best to be the result of the mutation. The proposed method is particularly suitable for searchspaces withexpensive evaluation functions, suchas arise in engineering design. Empirical results in several engineering de- sign domains demonstrate that the proposed method can significantly speed up the GA optimizer. |
Year | Venue | Keywords |
|---|---|---|
2000 | GECCO | evaluation function,genetic algorithm,engineering design,design optimization,genetic operator,information operations |
Field | DocType | Citations |
Genetic operator,Mathematical optimization,Crossover,Computer science,Meta-optimization,Artificial intelligence,Operator (computer programming),Engineering design process,Machine learning,Genetic algorithm,Speedup | Conference | 30 |
PageRank | References | Authors |
2.74 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Khaled Rasheed | 1 | 86 | 18.10 |
| Haym Hirsh | 2 | 1839 | 277.74 |