Many metaheuristics have difficulty exploring their search space comprehensively. Exploration time and efficiency are highly dependent on the size and the ruggedness of the search...
We investigate a family of inference problems on Markov models, where many sample paths are drawn from a Markov chain and partial information is revealed to an observer who attemp...
Daniel Sheldon, M. A. Saleh Elmohamed, Dexter Koze...
Evolutionary multi-objective optimization (EMO) methodologies, suggested in the beginning of Nineties, focussed on the task of finding a set of well-converged and well-distribute...
An important challenge within hyper-heuristic research is to design search methodologies that work well, not only across different instances of the same problem, but also across di...
Edmund K. Burke, Timothy Curtois, Matthew R. Hyde,...
There is an increasing quantity of data with uncertainty arising from applications such as sensor network measurements, record linkage, and as output of mining algorithms. This un...