Name
Abstract | ||
|---|---|---|
We present our asynchronous implementation of the LM-CMA-ES algorithm, which is a modern evolution strategy for solving complex large-scale continuous optimization problems. Our implementation brings the best results when the number of cores is relatively high and the computational complexity of the fitness function is also high. The experiments with benchmark functions show that it is able to overcome its origin on the Sphere function, reaches certain thresholds faster on the Rosenbrock and Ellipsoid function, and surprisingly performs much better than the original version on the Rastrigin function. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICMLA.2015.97 | 2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) |
Field | DocType | Volume |
Computer science,Rastrigin function,Evolution strategy,CMA-ES,Artificial intelligence,Continuous optimization,Asynchronous communication,Mathematical optimization,Parallel algorithm,Algorithm,Fitness function,Machine learning,Computational complexity theory | Journal | abs/1510.00419 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Viktor Arkhipov | 1 | 3 | 0.82 |
| Maxim Buzdalov | 2 | 141 | 25.29 |
| Anatoly Shalyto | 3 | 98 | 20.06 |