Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Computation
A study of some implications of the no free lunch theorem
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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Search algorithms are often compared by the optimization speed achieved on some sets of cost functions. Here some properties of algorithms' optimization speed are introduced and discussed. In particular, we show that determining whether a set of cost functions F admits a search algorithm having given optimization speed is an NP-complete problem. Further, we derive an explicit formula to calculate the best achievable optimization speed when F is closed under permutation. Finally, we show that the optimization speed achieved by some well-know optimization techniques can be much worse than the best theoretical value, at least on some sets of optimization benchmarks.