Evolutionary Constrained Optimization by Rituparna DattaEvolutionary Constrained Optimization by Rituparna Datta

Evolutionary Constrained Optimization

byRituparna DattaEditorKalyanmoy Deb

Hardcover | December 30, 2014

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This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research.
Rituparna Datta is a postdoctoral research fellow with the Robot Intelligence Technology (RIT) Laboratory at the Korea Advanced Institute of Science and Technology (KAIST). He earned his PhD in Mechanical Engineering at Indian Institute of Technology (IIT) Kanpur and thereafter worked as a Project Scientist in the Smart Materials, Stru...
Title:Evolutionary Constrained OptimizationFormat:HardcoverDimensions:319 pagesPublished:December 30, 2014Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:8132221834

ISBN - 13:9788132221838

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Table of Contents

A Critical Review of Adaptive Penalty Techniques in Evolutionary Computation.- Ruggedness Quantifying for Constrained Continuous Fitness Landscapes.- Trust Regions in Surrogate-Assisted Evolutionary Programming for Constrained Expensive Black-Box Optimization.- Ephemeral Resource Constraints in Optimization.- Incremental Approximation Models for Constrained Evolutionary Optimization.- Efficient Constrained Optimization by the ε Constrained Differential Evolution with Rough Approximation.- Analyzing the Behaviour of Multi-Recombinative Evolution Strategies Applied to a Conically Constrained Problem.- Locating Potentially Disjoint Feasible Regions of a Search Space with a Particle Swarm Optimizer.- Ensemble of Constraint Handling Techniques for Single Objective Constrained Optimization.- Evolutionary Constrained Optimization: A Hybrid Approach.

Editorial Reviews

"The book contains ten chapters, each one dealingwith various aspects of the current state of the art in the field ofevolutionary optimization algorithms applied to general constrainedoptimization problems. . It will be useful for practitioners dealing with hardconstrained optimization problems, as well as researchers and graduate studentsin the computer science and engineering fields." (Petrica Pop, ComputingReviews, October, 2015)