Abstract. This paper presents a neural-evolutionary framework for the simulation of market models in a bounded rationality scenario. Each agent involved in the scenario make use of...
In this paper we present a framework for using multi-layer perceptron (MLP) networks in nonlinear generative models trained by variational Bayesian learning. The nonlinearity is h...
Probabilistic inference problems arise naturally in distributed systems such as sensor networks and teams of mobile robots. Inference algorithms that use message passing are a nat...
In this paper, we focus on the implementation of a localization algorithm for sensor networks using a discrete event simulation (DES) architecture. In this implementation, DES is ...
This tutorial will focus on several new real-world applications that have been developed using an integrated set of methods, including Tabu Search, Scatter Search, Mixed Integer P...
Jay April, Marco Better, Fred Glover, James P. Kel...