No free lunch theorems for optimization Export

@article{Wolpert&Macready02, abstract = {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}, author = {Wolpert, D. H. and Macready, W. G.}, citeulike-article-id = {404286}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/4235.585893}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=585893}, day = {06}, doi = {10.1109/4235.585893}, journal = {Evolutionary Computation, IEEE Transactions on}, month = {August}, number = {1}, pages = {67--82}, posted-at = {2009-10-11 23:03:49}, priority = {2}, title = {No free lunch theorems for optimization}, url = {http://dx.doi.org/10.1109/4235.585893}, volume = {1}, year = {2002} }

TY - JOUR ID - Wolpert&Macready02 L3 - citeulike-article-id:404286 N2 - 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 IS - 1 JF - Evolutionary Computation, IEEE Transactions on EP - 82 TI - No free lunch theorems for optimization VL - 1 SP - 67 AU - Wolpert, DH AU - Macready, WG PY - 2002/08/06/ UR - http://dx.doi.org/10.1109/4235.585893 ER -