No Free Lunch in Search and Optimization
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In computing, there are circumstances in which the outputs of all procedures solving a particular type of problem are statistically identical. A colorful way of describing such a circumstance, introduced by David Wolpert and William G. Macready in connection with the problems of search and optimization, is to say that there is no free lunch. Wolpert had previously derived no free lunch theorems for machine learning (statistical inference). Before Wolpert's article was published, Cullen Schaffer had summarized a preprint version of this work of Wolpert's, but used different terminology. In the…mehr

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In computing, there are circumstances in which the outputs of all procedures solving a particular type of problem are statistically identical. A colorful way of describing such a circumstance, introduced by David Wolpert and William G. Macready in connection with the problems of search and optimization, is to say that there is no free lunch. Wolpert had previously derived no free lunch theorems for machine learning (statistical inference). Before Wolpert's article was published, Cullen Schaffer had summarized a preprint version of this work of Wolpert's, but used different terminology. In the "no free lunch" metaphor, each "restaurant" (problem-solving procedure) has a "menu" associating each "lunch plate" (problem) with a "price" (the performance of the procedure in solving the problem). The menus of restaurants are identical except in one regard the prices are shuffled from one restaurant to the next.