Short Description: Artificial neural network is designed with the goal of building intelligent machines to solve complex perceptual problems, such as pattern classification, by mimicking the networks of real neurons in the human brain. To achieve better performance over a single neural network, ensembles construction with several neural networks has become a nutritious filed of research. This book presents the basics of ensemble construction and discusses several existing ensemble methods. A large number of benchmark problems from different application domains were considered for evaluating the methods. After the discussion of the existing methods three new different techniques of ensemble constructions are explained in three different chapters. Last two chapters of this book are to discuss the new techniques on a common ground and draw the conclusions of this work together with the outline of future directions of research opened by this work.

Short Description: Genes are the basic blue print of life in an organism and a set of genes that interact with each other to control a specific cell function is termed as Gene Regulatory Network (GRN). GRN inference is the reverse engineering approach to uncover the dynamic and intertwined nature of gene regulation in cellular systems analyzing gene expression. GRN inference is a computational intelligence task and a number of methods have been investigated those are categorized into two different approaches. In information theoretic approach, dependencies among genes are measured and then network is inferred employing individual inference technique. In model based approach, parameter estimation of S-System model is a high dimensional optimization task. This book provides GRN inference basics and comprehensive study of major inference methods in both approaches. Several Swarm Intelligence methods are discussed and adapted to optimize S-System parameters. Prominent methods are evaluated on benchmark gene expression datasets and are compared on the basis of standard measures. The book will be helpful for basic knowledge and research foundation on GRN inference which is a promising field of research.