Name
Abstract | ||
|---|---|---|
Following a recent call for a suite of benchmarks for genetic programming, we investigate the criteria for a meaningful dynamic benchmark for GP. We explore the design of a dynamic benchmark for symbolic regression, based on semantic distance between evaluated functions, where larger distances serve as a proxy for greater environmental change. We do not find convincing evidence that lower semantic distance is a good proxy for greater ease in adapting to a change. We conclude that due to fundamental characteristics of GP, it is difficult to come up with a single dynamic benchmark problem which is generally applicable. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2464576.2464649 | GECCO (Companion) |
Keywords | Field | DocType |
good proxy,convincing evidence,meaningful dynamic benchmark,genetic programming,greater ease,lower semantic distance,single dynamic benchmark problem,dynamic benchmark,semantic distance,greater environmental change,larger distance | Semantic similarity,Proxy (climate),Dynamical optimization,Mathematical optimization,Suite,Computer science,Genetic programming,Artificial intelligence,Symbolic regression,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cliodhna Tuite | 1 | 0 | 0.34 |
| michael o neill | 2 | 599 | 60.93 |
| Anthony Brabazon | 3 | 918 | 98.60 |