Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applicati...
Abstract. In this article we present EANT2, a method that creates neural networks (NNs) by evolutionary reinforcement learning. The structure of NNs is developed using mutation ope...
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...
In the longest common subsequence problem the task is to find the longest sequence of letters that can be found as subsequence in all members of a given finite set of sequences....
Abstract. When a set of servers are available for a certain client-server style service, a client selects one of the servers using some server selection algorithm. The best-server ...