Preliminary Statement on the Current Progress of Multi-Objective Evolutionary Algorithm Performance Measurement


Although multi-objective evolutionary algorithm techniques are becoming mature, benchmark measures for evaluating the algorithms still require further research, as convergence theories can hardly be applied here and the only practical method for performance comparison is through benchmark tests. This paper investigates the current progress on multi-objective evolutionary algorithm performance measurement. The paper is focused on identifying deficiencies existing in the current performance measure techniques. It is shown that, whilst some performance indicators are conclusive and consistent, it is critical for some cases to include the 'diversity' indicator in a benchmark test. Some possible ways forward are also identified.