Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Tecno-streams approach for content-based image retrieval

Content-based image retrieval (CBIR) is based on a collective method of various techniques for retrieving images through the appropriate features, such as color, texture and shape. Despite of selecting an effective collection of features for each image, it is significant to use appropriate and relevant criterion to measure the similarity between a query image and all the available images in the dataset. In this paper we are introducing a new and effective similarity measurement criterion. In other words, in order to retrieve the images in an impressive way, an artificial immune system (AIS) clustering algorithm, called Tecno-Streams, is proposed for contentbased image retrieval. According to experimental results the proposed image retrieval immune-based system is more applicable and effective than the other methods such as histogram based criterion.</p