The "No Free Lunch" (NFL) theorems state that in average each algorithm has the same performance, when no a priori knowledge of single-objective cost function f is assumed. This paper extends the NFL theorems to the case of multi-objective optimization. Further it is shown that even in cases of a priori knowledge, when the performance measure is related to the set of extrema points sampled so far, the NFL theorems still hold. However, a procedure for obtaining function-dependent algorithm performance can be constructed, the so-called tournament performance, which is able to gain different performance measures for different multiobjective algorithms.
CITATION STYLE
Köppen, M. (2003). On the benchmarking of multiobjective optimization algorithm. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2773 PART 1, pp. 379–385). Springer Verlag. https://doi.org/10.1007/978-3-540-45224-9_53
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