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On the benchmarking of multiobjective optimization algorithm

: Köppen, M.

Palade, V.:
Knowledge-based intelligent information and engineering systems. Pt.1 : 7th international conference ;proceedings / KES 2003, Oxford, UK, September 3 - 5, 2003
Berlin: Springer, 2003 (Lecture Notes in Artificial Intelligence 2773)
ISBN: 3-540-40803-7
ISSN: 0302-9743
International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES) <7, 2003, Oxford>
Conference Paper
Fraunhofer IPK ()

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.