The increasing popularity of metaheuristic algorithms has attracted a great deal of attention in algorithm analysis and performance evaluations. No-free-lunch theorems are of both theoretical and practical importance, while many important studies on convergence analysis of various metaheuristic algorithms have proven to be fruitful. This paper discusses the recent results on no-free-lunch theorems and algorithm convergence, as well as their important implications for algorithm development in practice. Free lunches may exist for certain types of problem. In addition, we will highlight some open problems for further research
The No Free Lunch theorems are often used to argue that domain specific knowledge is required to des...
We extend previous results concerning Black-Box search algorithms, presenting new theoretical tools ...
The “no-free lunch theorems ” essentially say that for any two algorithms A and B, there are “as man...
The increasing popularity of metaheuristic algorithms has attracted a great deal of attention in alg...
[...] Thus not only our reason fails us in the discovery of the ultimate connexion of causes and eff...
The No-Free-Lunch theorem is a fundamental result in the field of black-box function optimization. R...
This letter discusses the recent paper "Some technical remarks on the proof of the 'No Free Lunch' t...
It is often claimed that Evolutionary Algorithms are superior to other optimization techniques, in p...
It is often claimed that Evolutionary Algorithms are superior to other optimization techniques, in p...
Abstract — The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/s...
The No Free Lunch (NFL) theorem for search and optimisation states that averaged across all possible...
The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/search algor...
Function optimisation is a major challenge in computer science. The No Free Lunch theorems state tha...
International audienceThis paper analyses extensions of No-Free-Lunch (NFL) theorems to countably in...
We extend previous results concerning Black-Box search algorithms, presenting new theoretical tools ...
The No Free Lunch theorems are often used to argue that domain specific knowledge is required to des...
We extend previous results concerning Black-Box search algorithms, presenting new theoretical tools ...
The “no-free lunch theorems ” essentially say that for any two algorithms A and B, there are “as man...
The increasing popularity of metaheuristic algorithms has attracted a great deal of attention in alg...
[...] Thus not only our reason fails us in the discovery of the ultimate connexion of causes and eff...
The No-Free-Lunch theorem is a fundamental result in the field of black-box function optimization. R...
This letter discusses the recent paper "Some technical remarks on the proof of the 'No Free Lunch' t...
It is often claimed that Evolutionary Algorithms are superior to other optimization techniques, in p...
It is often claimed that Evolutionary Algorithms are superior to other optimization techniques, in p...
Abstract — The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/s...
The No Free Lunch (NFL) theorem for search and optimisation states that averaged across all possible...
The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/search algor...
Function optimisation is a major challenge in computer science. The No Free Lunch theorems state tha...
International audienceThis paper analyses extensions of No-Free-Lunch (NFL) theorems to countably in...
We extend previous results concerning Black-Box search algorithms, presenting new theoretical tools ...
The No Free Lunch theorems are often used to argue that domain specific knowledge is required to des...
We extend previous results concerning Black-Box search algorithms, presenting new theoretical tools ...
The “no-free lunch theorems ” essentially say that for any two algorithms A and B, there are “as man...