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Adaptation and hybridization in computational intelligence

Author: Iztok Fister
Publisher: Cham [Switzerland] : Springer, 2015. ©2015
Series: Adaptation, learning and optimization, volume 18.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Printed edition:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Iztok Fister
ISBN: 9783319144009 3319144006 3319143999 9783319143996
OCLC Number: 900781348
Description: 1 online resource.
Contents: 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.
Series Title: Adaptation, learning and optimization, volume 18.
Responsibility: Iztok Fister, Iztok Fister Jr., editors.

Abstract:

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.

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