Finite domain propagation solving, the basis of constraint programming (CP) solvers, allows building very high-level models of problems, and using highly specific inference encapsu...
Several population-based methods (with origins in the world of evolutionary strategies and estimation-of-distribution algorithms) for black-box optimization in continuous domains ...
An explicit exploration strategy is necessary in reinforcement learning (RL) to balance the need to reduce the uncertainty associated with the expected outcome of an action and the...
Routing in computer networks is a nonlinear combinatorial optimization problem with numerous constraints and is classified as an NP-complete problem. There are certain important Qo...
We focus on the handling of overlapping solutions in evolutionary multiobjective optimization (EMO) algorithms. First we show that there exist a large number of overlapping soluti...