Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Introductory Discrete Mathematics
Introductory Discrete Mathematics
On the futility of blind search: An algorithmic view of “no free lunch”
Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Optimization, block designs and no free lunch theorems
Information Processing Letters
Focused no free lunch theorems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Iterative feature construction for improving inductive learning algorithms
Expert Systems with Applications: An International Journal
Free lunches for function and program induction
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Free lunches for neural network search
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Optimization, block designs and No Free Lunch theorems
Information Processing Letters
A study of some implications of the no free lunch theorem
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Optimization speed and fair sets of functions
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Free lunches on the discrete Lipschitz class
Theoretical Computer Science
Evolutionary Computation
Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators
Information Sciences: an International Journal
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We identify classes of functions for which a No Free Lunch result does and does not hold, with particular emphasis on the relationship between No Free Lunch and problem description length. We show that a NFL result does not apply to a set of functions when the description length of the functions is sufficiently bounded. We consider sets of functions with non-uniform associated probability distributions, and show that a NFL result does not hold if the probabilities are assigned according either to description length or to a Solomonoff-Levin distribution. We close with a discussion of the conditions under which NFL can apply to sets containing an infinite number of functions.