Adaptation and Hybridization in Computational Intelligence (Adaptation, Learning, and Optimization)

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 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.