Hyper-heuristics (HHs) are heuristics that work with an arbitrary set of search operators or algorithms and combine these algorithms adaptively to achieve a better performance tha...
We present a Semantic Optimized Service Discovery (SemOSD) approach capable of handling Web service search requests on a fine-grained level of detail where we augment semantic ser...
We show that for even quasi-concave objective functions the worst-case distribution, with respect to a family of unimodal distributions, of a stochastic programming problem is a u...
We consider a robust model proposed by Scarf, 1958, for stochastic optimization when only the marginal probabilities of (binary) random variables are given, and the correlation be...
We present a novel search algorithm which is suitable for optimizing functions with a high-dimensional discrete-valued parameter vector. The algorithm is designed to find a funct...
Tuukka Toivonen, Loren Merritt, Ville Ojansivu, Ja...