The paper presents distributed and parallel -approximation algorithms for covering problems, where is the maximum number of variables on which any constraint depends (for example...
Constraint Programming is an attractive approach for solving AI planning problems by modelling them as Constraint Satisfaction Problems (CSPs). However, formulating effective cons...
Andrea Rendl, Ian Miguel, Ian P. Gent, Peter Grego...
The main aim of randomized search heuristics is to produce good approximations of optimal solutions within a small amount of time. In contrast to numerous experimental results, th...
Tobias Friedrich, Nils Hebbinghaus, Frank Neumann,...
This work discusses the issue of approximation in point set matching problems. In general, one may have two classes of approximations when tackling a matching problem: a representa...
In continuous optimisation, surrogate models (SMs) are used when tackling real-world problems whose candidate solutions are expensive to evaluate. In previous work, we showed that...