Name
Abstract | ||
|---|---|---|
We give a definition for step size optimality in multiobjective optimization and visualize the optimal step sizes for a few two-dimensional example constellations. After that, we try to engineer a step size adaptation mechanism that also works in the real world. For this mechanism, we employ the self-adaptation of mutation strength, which is simple and well-known from single-objective optimization. The resulting approach obtains better results than simulated binary crossover and polynomial mutation on the bi-objective BBOB testbed. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-54157-0_45 | EMO |
Field | DocType | Citations |
Mathematical optimization,Crossover,Polynomial,Computer science,Testbed,Multi-objective optimization,Binary number | Conference | 1 |
PageRank | References | Authors |
0.38 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Wessing | 1 | 87 | 8.05 |
| Rosa Pink | 2 | 1 | 0.38 |
| Kai Brandenbusch | 3 | 1 | 0.72 |
| Günter Rudolph | 4 | 219 | 48.59 |