The constraint paradigm provides powerful concepts to represent and solve different kinds of planning problems, e. g. factory scheduling. Factory scheduling is a demanding optimiz...
Abstract. Machine learning can be utilized to build models that predict the runtime of search algorithms for hard combinatorial problems. Such empirical hardness models have previo...
Frank Hutter, Youssef Hamadi, Holger H. Hoos, Kevi...
We consider the following one- and two-dimensional bucketing problems: Given a set S of n points in R1 or R2 and a positive integer b, distribute the points of S into b equal-size ...
Pankaj K. Agarwal, Binay K. Bhattacharya, Sandeep ...
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...
Block-matching motion estimation plays an important role in video coding and faster, more robust and more effective search algorithms are needed. Recently, a great number of fast ...