Theoretical Computer Science - Natural computing
On classes of functions for which No Free Lunch results hold
Information Processing Letters
Two broad classes of functions for which a no free lunch result does not hold
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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 speed and fair sets of functions
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A No Free Lunch theorem for multi-objective optimization
Information Processing Letters
Unbiased black box search algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolutionary Computation
Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators
Information Sciences: an International Journal
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Proofs and empirical evidence are presented which show that a subset of algorithms can have identical performance over a subset of functions, even when the subset of functions is not closed under permutation. We refer to these as focused sets. In some cases focused sets correspond to the orbit of a permutation group; in other cases, the focused sets must be computed heuristically. In the smallest case, two algorithms can have identical performance over just two functions in a focused set. These results particularly exploit the case where search is limited to m steps, where m is significantly smaller than the size of the search space.