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The rst No Free Lunch (NFL) theorems were introduced in [9], in the contextof supervised machine learning. These theorems were then popularized in [8],based on a preprint version of [9]. Loosely speaking, these original theorems canbe viewed as a formalization and elaboration of concerns about the legitimacyof inductive inference, concerns that date back to David Hume (if not earlier).Shortly after these original theorems were published, additional NFL theoremsthat apply to search were introduced in [12].The NFL theorems have stimulated lots of subsequent work, with over 2500citations of [12] alone by spring 2012 according to Google Scholar. However ar-guably much of that research has missed the most important implications of thetheorems. As stated in [12], the primary importance of the NFL theorems forsearch is what they tell us about “the underlying mathematical ‘skeleton’ of op-timization theory before the ‘esh’ of the probability distributions of a particularcontext and set of optimization problems are imposed”. So in particular, while theNFL theorems have strong implications if one believes in a uniform distributionover optimization problems, in no sense should they be interpreted as advocatingsuch a distribution.1

Ubiquity symposium: Evolutionary computation and the processes of life: what the no free lunch theorems really mean: how to improve search algorithms