Adaptation and Hybridization in Computational Intelligence

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.

Presents recent research in self-adaptation techniques in computational intelligence algorithms and applications as well as theoretical analysis Provides both theoretical treatments and real-world insights gained by experience Comprehensive reference for researchers, practitioners and advanced-level students interested in using computational intelligence algorithms in real-world applications

Inhalt
Adaptation and Hybridization in Nature-Inspired Algorithms.- Adaptation in the Differential Evolution.- On the Mutation Operators in Evolution Strategies.- Adaptation in Cooperative Coevolutionary Optimization.- Study of Lagrangian and Evolutionary Parameters in Krill Herd Algorithm.- Solutions of Non-Smooth Economic Dispatch Problems by Swarm Intelligence.- Hybrid Artifcial Neural Network for Fire Analysis of Steel Frames.- A Differential Evolution Algorithm with A Variable Neighborhood Search for Constrained Function Optimization.- A Memetic Differential Evolution Algorithm for the Vehicle Routing Problem with Stochastic Demands.