Name
Abstract | ||
|---|---|---|
The concept of information gain has been adopted as tool to study the effectiveness of population-based optimizers; using this approach, it is possible to infer convergence properties for population-based optimizers. The experimental results have shown characteristic phase transition between exploration and exploitation phase during the evolutionary process and, moreover, the evidence that gain maximization offers a robust theoretical framework to study the convergence of stochastic optimizers. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CEC.2010.5586044 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
convergence,entropy,evolutionary computation,optimisation,stochastic processes,convergence properties,entropic divergence,evolutionary process,gain maximization,information gain,phase transition,population based optimization algorithm,stochastic optimizer | Convergence (routing),Population,Mathematical optimization,Algorithm design,Computer science,Stochastic process,Evolutionary computation,Minification,Artificial intelligence,Covariance matrix,Maximization,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-6909-3 | 1 | 0.35 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| vincenzo cutello | 1 | 553 | 57.63 |
| Giuseppe Nicosia | 2 | 479 | 46.53 |
| Mario Pavone | 3 | 212 | 19.41 |
| Giovanni Stracquadanio | 4 | 45 | 6.86 |