Factored evolutionary algorithms (FEAs) are a new class of evolutionary search-based
optimization algorithms that have successfully been applied to various problems, such as …

Wavelet neural networks (WNNs) are a new class of neural networks (NNs) that has been
developed using a combined method of multi-layer artificial neural networks and wavelet …

Nature is an intrinsic source of inspiration for researchers and scientists working in the area
of optimization. This chapter introduces three swarm-intelligence-based nature-inspired …

Computerized tomography provides valuable information for imaging the ionospheric
electron density distribution. We use a wavelet neural network with a particle swarm …

The rapid development of parallel and distributed computing paradigms has brought about
great revolution in computing. Thanks to the intrinsic parallelism of evolutionary computation …

In the past few years, Deep Learning has become the method of choice for producing state-
of-the-art results on machine learning problems involving images, text, and speech. The …

In this paper we propose several approximation algorithms for the problems of full and
partial abductive inference in Bayesian belief networks. Full abductive inference is the …

Abductive inference in Bayesian networks, is the problem of finding the most likely joint
assignment to all non-evidence variables in the network. Such an assignment is called the …

Navigation via olfaction (scent) is one of the most primitive forms of exploration used by
organisms. Machine olfaction is a growing field within sensing systems and AI and many of …

Bayesian networks are powerful probabilistic models that have been applied to a variety of
tasks. When applied to classification problems, Bayesian networks have shown competitive …