Name
Abstract | ||
|---|---|---|
While a lot of attention is usually devoted to the study of different components of evolutionary algorithms or the creation of heuristic operators, little effort is being directed at how these algorithms are actually implemented. However, the efficient implementation of any application is essential to obtain a good performance, to the point that performance improvements obtained by changes in implementation are usually much bigger than those obtained by algorithmic changes, and they also scale much better. In this paper we will present and apply usual methodologies for performance improvement to evolutionary algorithms, and show which implementation options yield the best results for a certain problem configuration and which ones scale better when features such as population or chromosome size increase. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21498-1_42 | IWANN (2) |
Keywords | Field | DocType |
performance improvement,algorithmic change,implementation matter,efficient implementation,best result,good performance,certain problem configuration,best practice,different component,chromosome size increase,evolutionary algorithm,implementation option | Population,Evolutionary algorithm,Computer science,Artificial intelligence,Operator (computer programming),Sorting algorithm,Management science,Heuristic,Best practice,Industrial engineering,Size increase,Machine learning,Performance improvement | Conference |
Volume | ISSN | Citations |
6692 | 0302-9743 | 13 |
PageRank | References | Authors |
0.87 | 12 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. J. Merelo | 1 | 363 | 33.51 |
| G. Romero | 2 | 81 | 7.38 |
| M. G. Arenas | 3 | 48 | 6.27 |
| P. A. Castillo | 4 | 134 | 13.95 |
| A. M. Mora | 5 | 99 | 10.00 |
| J. L. Laredo | 6 | 69 | 5.89 |