| 1 | | Run-length distributions give insight into the required algorithmic effort (in terms of function evaluations or wall-clock time) for reaching a certain target. |
| 2 | | |
| 3 | | The empirical cumulative distribution function (ECDF) can be calculated from several runs of an optimization algorithm. From a first-hit graph several targets will be defined in the objective function and it will be measured when a target is hit by an algorithm. The proportion of all these hits forms the distribution. |
| | 1 | The algorithm iterator should execute an algorithm instance over and over until a certain stopping criterion is met. The stopping criteria are: |
| | 2 | * Maximum execution time reached |
| | 3 | * Maximum number of evaluated solutions |
| | 4 | * A certain target fitness was achieved |
