No Free Lunch Theorems for Optimization
NFL理论其实是大家都潜意识中都有的一个概念，“没有免费午餐”其实就是说一个算法在为解决一类问题做出优化时，在其他算法上必然会付出效果降低的代价。即没有一个算法能完美解决所有问题。
原文摘要：
A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of “no free lunch” (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem. Applications of the NFL theorems to information-theoretic aspects of optimization and benchmark measures of performance are also presented. Other issues addressed include time-varying optimization problems and a priori “head-to-head” minimax distinctions between optimization algorithms, distinctions that result despite the NFL theorems’ enforcing of a type of uniformity over all algorithms.
