An ensemble method for performance metrics in multiobjective evolutionary algorithms
Zhenan He, Gary G. Yen · 2011
Evolutionary algorithms have been effectively exploited to solve multiobjective optimization problems. In literature, a heuristic approach is often taken. For a chosen benchmark problem, the performance of multiobjective evolutionary algorithms (MOEAs) is evaluated via some heuristic chosen performance metrics. The conclusion is then drawn based on statistical findings given the preferable choices of performance metrics. The conclusion, if any, is often indecisive and reveals no insight pertaining to specific problem characteristics that the underlying MOEA could perform the best. In this paper, we introduce an ensemble method to compare MOEAs by combining a number of performance metrics using double elimination tournament selection. Double elimination design allows characteristically poor performance of a quality algorithm under the special environment to still be able to win it all. Experimental results show that the proposed metrics ensemble can provide a more comprehensive comparison among various MOEAs than what could be obtained from single performance metric alone.