Evolutionary Constrained Optimization (Infosys Science Foundation Series in Applied Sciences and Engineering)

Datta, Rituparna (EDT)/ Deb, Kalyanmoy (EDT)

Springer, India, Private Ltd（2016/08発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 319 p.
• 言語 ENG
• 商品コード 9788132235057
• DDC分類 006

Full Description

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.

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.