Name
Abstract | ||
|---|---|---|
Quantitative quality assessment of approxima- tions of the Pareto-optimal set is an important issue in comparing the performance of multiob- jective evolutionary algorithms. Most popular are methods that assign each approximation set a vector of real numbers that reflect different as- pects of the quality. In this study, we investigate this type of quality assessment from a theoreti- cal point of view. We provide a rigorous analysis of limitations and suggest a mathematical frame- work on the basis of which existing techniques are classified and discussed. In single-objective optimization, we can define quality by means of the objective function: the smaller (or greater) the value, the better the solution. In contrast, quality is itself multiobjective in the presence of several optimization criteria. The goal is to find an approximation set that is as close as possible to the optimal front, covers a wide range of diverse solutions, etc. Therefore, it is difficult to define appropriate quality measures for approximation sets, and as a consequence there is no common agreement about what |
Year | Venue | Keywords |
|---|---|---|
2002 | GECCO | multiobjective optimizers,quality assessment,objective function,multiobjective optimization,evolutionary algorithm |
Field | DocType | ISBN |
Mathematical optimization,Evolutionary algorithm,Computer science,Artificial intelligence,Real number,Machine learning | Conference | 1-55860-878-8 |
Citations | PageRank | References |
46 | 3.31 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eckart Zitzler | 1 | 4678 | 291.01 |
| Marco Laumanns | 2 | 1452 | 108.63 |
| Lothar Thiele | 3 | 14025 | 957.82 |
| Carlos M. Fonseca | 4 | 1906 | 321.37 |
| Viviane Grunert da Fonseca | 5 | 148 | 12.58 |