A hybrid evolutionary algorithm (EA) for the p-median problem consist of two stages, each of which is a steady-state hybrid EA. These EAs encode selections of medians as subsets o...
Coevolution can be used to adaptively choose the tests used for evaluating candidate solutions. A long-standing question is how this dynamic setup may be organized to yield reliab...
We define an algorithmic paradigm, the stack model, that captures many primal-dual and local-ratio algorithms for approximating covering and packing problems. The stack model is ...
The use of entropy as a cost function in the neural network learning phase usually implies that, in the back-propagation algorithm, the training is done in batch mode. Apart from t...
This paper proposes a set of novel multicast algorithms for m-D mesh overlay networks that can achieve shorter multicast delay and less resource consumptions. In contrast to previo...