Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
In this paper, an object-oriented unified optimization framework (UOF) for general problem optimization is proposed. Based on evolutionary algorithms, numerical deterministic meth...
Evolutionary algorithms (EAs) have been applied with success to many numerical and combinatorial optimization problems in recent years. However, they often lose their effectivenes...
Abstract—Models that can efficiently, compactly, and semantically represent potential users are important tools for human-robot interaction applications. We model a person as a p...
In this work, a novel optimization framework is proposed that allows the improvement of Quality of Service levels in TCP/IP based networks, by configuring the routing weights of ...
Miguel Rocha, Pedro Sousa, Paulo Cortez, Miguel Ri...