The No Free Lunch and Realistic Search Algorithms

ABSTRACT The No-Free-Lunch theorems (NFLTs) are criticized for being too general to be of any relevance to the real world scenario. This paper investigates, both formally and empirically, the implications of the NFLTs for realistic search algorithms. In the first part of the paper, by restricting ourselves to a specific performance measure, we derive a new NFL result for a class of problems which is not closed under permutations. In the second part, we discuss properties of this set which are likely to be true for realistic search algorithms. We provide empirical support for this i

The No Free Lunch and Realistic Search Algorithms

The No-Free-Lunch theorems (NFLTs) are criticized for being too general to be of any relevance to the real world scenario. This paper investigates, both formally and empirically, the implications of the NFLTs for realistic search algorithms. In the first part of the paper, by restricting ourselves to a specific performance measure, we derive a new NFL result for a class of problems which is not closed under permutations. In the second part, we study, empirically, the properties of this set for realistic search algorithms. In particular, we propose that if one knows a function on which a realistic algorithm performs well, it is possible to estimate its performance on any other function. 1