Guest editorial: revised selected papers from the LION 8 conference

In February 2014, we had the honor of organizing the eighth installment of the conference series “Learning and Intelligent Optimization” (LION) in Gainesville, Florida, USA. As always, the work presented was at the forefront of research performed between the fields of Artificial Intelligence, Mathematical Programing and Optimization, and Algorithmic Design. The conference was very successful in attracting researchers from around the world who have been pushing the boundaries in the intersection of the above fields, among others. As has become tradition from the earlier, very successful LION conferences, we are happy to dedicate this special issue to extended versions of a selected sample of the work presented in LION 8 in February 2014. For the remainder of the contributions submitted and presented during LION 8, we refer the interested reader to [1]. The spectrum of the topics covered in this special issue ranges from facility location and routing, to job scheduling and robotics, while the techniques and approaches come from fields as diverse as algorithm portfolio techniques, evolutionary algorithms, and hybrid metaheuristics. More specifically this special issue contains the following contributions. The first paper, Bayesian Optimization for Learning Gaits Under Uncertainty by Roberto Calandra, Andre Seyfarth, Jan Peters, and Marc Peter Deisenroth, deals with the problem