This paper reviews the use of evolutionary algorithms (EAs) to optimize artificial neural
networks (ANNs). First, we briefly introduce the basic principles of artificial neural networks …

Artificial neural networks (ANN) have been widely used for thermal analysis of heat
exchangers during the last two decades. In this paper, the applications of ANN for thermal …

We evaluate and compare several forecasting techniques using no exogenous inputs for
predicting the solar power output of a 1MWp, single-axis tracking, photovoltaic power plant …

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to
a new paradigm for evolutionary computation, named estimation of distribution algorithms …

Artificial neural networks (ANNs) are flexible computing frameworks and universal
approximators that can be applied to a wide range of time series forecasting problems with a …

Despite the fact that feedforward artificial neural networks (ANNs) have been a hot topic of
research for many years there still are certain issues regarding the development of an ANN …

Artificial neural networks (ANNs) becomes very popular tool in hydrology, especially in
rainfall–runoff modelling. However, a number of issues should be addressed to apply this …

The potential of multiple linear regression (MLR) and artificial neural network (ANN)
techniques in predicting transient water levels over a groundwater basin were compared …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system; neural networks provide a model …

Accurate information about groundwater level prediction is crucial for effective planning and
management of groundwater resources. In the present study, the Artificial Neural Network …