Name
Abstract | ||
|---|---|---|
Estimation-of-distribution algorithm using Cauchy sampling distribution is compared with the iterative prototype optimization algorithm with evolved improvement steps. While Cauchy EDA is better on unimodal functions, iterative prototype optimization is more suitable for multimodal functions. This paper compares the results for both algorithms in more detail and adds to the understanding of their key features and differences. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1830761.1830792 | GECCO (Companion) |
Keywords | Field | DocType |
unimodal function,iterative prototype optimization algorithm,cauchy eda,bbob noiseless testbed,multimodal function,key feature,iterative prototype optimization,improvement step,estimation-of-distribution algorithm,ppoems algorithm,cauchy sampling distribution,poems,distributed algorithm,benchmarking,estimation of distribution algorithm,cauchy distribution | Sampling distribution,Estimation of distribution algorithm,Computer science,Testbed,Cauchy distribution,Evolution strategy,Artificial intelligence,CMA-ES,Population-based incremental learning,Benchmarking,Mathematical optimization,Algorithm,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Petr Pošík | 1 | 210 | 15.44 |
| Jiří Kubalik | 2 | 14 | 2.57 |