Name
Abstract | ||
|---|---|---|
We present a statistical model of empirical optimization that admits the creation of algorithms with explicit and intuitively defined desiderata. Because No Free Lunch theorems dictate that no optimization algorithm can be considered more efficient than any other when considering all possible functions, the desired function class plays a prominent role in the model. In particular, this provides a direct way to answer the traditionally difficult question of what algorithm is best matched to a particular class of functions. Among the benefits of the model are the ability to specify the function class in a straightforward manner, a natural way to specify noisy or dynamic functions, and a new source of insight into No Free Lunch theorems for optimization. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1162/evco.2008.16.3.289 | Evolutionary Computation |
Keywords | Field | DocType |
Mathematical optimization models,evolutionary optimization,estimation of distribution algorithms | Continuous optimization,Derivative-free optimization,Mathematical optimization,Vector optimization,Meta-optimization,Test functions for optimization,No free lunch in search and optimization,Multi-objective optimization,Artificial intelligence,Random optimization,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
16 | 3 | 1063-6560 |
Citations | PageRank | References |
1 | 0.35 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher K. Monson | 1 | 134 | 14.77 |
| Kevin D. Seppi | 2 | 335 | 41.46 |