Abstract—Some real-world optimization problems have hundreds or even thousands of decision variables. However, the effect that the scalability of parameters has in modern multiob...
Abstract. Probabilistic Neural Networks (PNNs) constitute a promising methodology for classification and prediction tasks. Their performance depends heavily on several factors, su...
Vasileios L. Georgiou, Sonia Malefaki, Konstantino...
This work describes a distributed framework for routing path optimization in Optical Burst-Switched (OBS) networks that loosely mimics the foraging behaviour of ants observed in na...
In this paper, a recurrent neural network based fuzzy inference system (RNFIS) for prediction is proposed. A recurrent network is embedded in the RNFIS by adding feedback connecti...
Abstract. In our previous studies, Genetic Programming (GP), Probabilistic Incremental Program Evolution (PIPE) and Ant Programming (AP) have been used to optimal design of Flexibl...