Bøger i Adaptation, Learning, and Optimization serien

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 provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.

An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods.

This book covers the most recent advances in the field of evolutionary multiobjective optimization. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.

An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods.

This carefully edited book takes a walk through recent advances in adaptation and hybridization in the Computational Intelligence (CI) domain. It consists of ten chapters that are divided into three parts. The first part illustrates background information and provides some theoretical foundation tackling the CI domain, the second part deals with the adaptation in CI algorithms, while the third part focuses on the hybridization in CI.This book can serve as an ideal reference for researchers and students of computer science, electrical and civil engineering, economy, and natural sciences that are confronted with solving the optimization, modeling and simulation problems. It covers the recent advances in CI that encompass Nature-inspired algorithms, like Artificial Neural networks, Evolutionary Algorithms and Swarm Intelligence ¿based algorithms.

This collection of recent studies spans a range of computational intelligence applications, emphasizing their application to challenging real-world problems. Covers Intelligent agent-based algorithms, Hybrid intelligent systems, Machine learning and more.

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 collection of recent studies spans a range of computational intelligence applications, emphasizing their application to challenging real-world problems. Covers Intelligent agent-based algorithms, Hybrid intelligent systems, Machine learning and more.

This book describes how the principle of self-sufficiency can be applied to a reconfigurable modular robotic organism.

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.

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

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.

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 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.

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.

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

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.

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 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.

This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms.

The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing.

The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing.

This edited volume is aimed to provide the readers with a brief background of agent based evolutionary search, recent developments and studies dealing with various levels of information abstraction and applications of agent based evo- tionary systems.

In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms.

This edited volume is aimed to provide the readers with a brief background of agent based evolutionary search, recent developments and studies dealing with various levels of information abstraction and applications of agent based evo- tionary systems.

Examining various schemes to implement artificial intelligence techniques in agents, this book proposes a general conceptual framework for the development of automation in human-agent environments that will help human-agent teams to work more effectively.

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents.

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents.