Physical Sciences and Mathematics Commons^™

Many optimization problems are multi-modal. In certain cases, we are interested in finding multiple locally optimal solutions rather than just a single optimum as is computed by traditional genetic algorithms (GAs). Several niching techniques have been developed that seek to find multiple such local optima. These techniques, which include sharing and crowding, are clearly powerful and useful. But they do not explicitly let the user control the number of local optima being computed, which we believe to be an important capability.

In this paper, we develop a method that provides, as an input parameter to niching, the desired number of …

Mobile devices have evolved to become computing platforms more similar to desktops and workstations than the cell phones and handsets of yesteryear. Unfortunately, today’s mobile infrastructures are mirrors of the wired past. Devices, apps, and networks impact one another, but a systematic approach for allowing them to cooperate is currently missing. We propose an approach that seeks to open key interfaces and to apply feedback and autonomic computing to improve both user experience and mobile system dynamics.

This paper investigates the challenge of integrating intelligent systems into varying computational platforms and application mixes while providing reactive (or soft real-time) response. We integrate Bayesian network computation with feedback control, thereby achieving our reactive objective. As a case study we investigate fault diagnosis using Bayesian networks. While we consider the likelihood weighting and junction tree propagation Bayesian network inference algorithms in some detail, we hypothesize that the techniques developed can be broadly applied to achieve reactive intelligent systems. In the empirical study of this paper we demonstrate reactive fault diagnosis for an electrical power system.

This paper considers the problem of providing, for computational processes, soft real-time (or reactive) response without the use of a hard real-time operating system. In particular, we focus on the problem of reactively computing fault diagnosis by means of different Bayesian network inference algorithms on non-real-time operating systems where low-criticality (background) process activity and system load is unpredictable.

To address this problem, we take in this paper a reconfigurable adaptive control approach. Computation time is modeled using an ARX model where the input consists of the maximum number of background processes allowed to run at any given time. To ensure …