Bøger i Adaptation, Learning, and Optimization serien

A combination of theoretical treatment and real-world insight introduce the field of computational intelligence in this valuable reference. Topics include neural networks, frameworks for optimization, parallelization of algorithms, and more.

This book offers a theoretical and empirical approach to data fusion, used in information retrieval in complex, diverse settings such as web and social networks, legal, enterprise and others. Discusses, analyzes and ealuates typical data fusion algorithms.

The fundamental theme of this book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. The book offers real-world insights into a variety of large-scale complex industrial applications.

This is the first book to present a computational intelligence architecture capable of learning in unsupervised, supervised, or reinforcement learning modes. It is also the first to cover applications of time scales mathematics to engineering applications.

From nature, we observe swarming behavior in the form of ant colonies, bird flocking, animal herding, honey bees, swarming of bacteria, and many more.

Written by leading experts, this book contains recent research on group search optimization with applications in structural design. It details the latest research work related with particle swarm optimizer algorithm and group search optimizer algorithm.

This book bridges the gap between computer science academics and traders, presenting state-of-the-art techniques in financial engineering using machine learning and evolutionary computation. Includes information on software for implementing solutions.

This book focuses on the different steps involved in the conception, implementation and application of Estimation of distribution algorithms (EDAs) that use Markov networks and undirected models in general.

This book explores multidimensional particle swarm optimization, a technique developed by the authors and presented in a well-defined algorithmic approach. All featured applications are supported with fully documented source code as well as sample datasets.

From nature, we observe swarming behavior in the form of ant colonies, bird flocking, animal herding, honey bees, swarming of bacteria, and many more.