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Evolutionary Constrained Optimization

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Evolutionary Constrained Optimization/ edited by Rituparna Datta, Kalyanmoy Deb.
其他作者:	Datta, Rituparna.
面頁冊數:	XVI, 319 p. 111 illus., 39 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-81-322-2184-5
ISBN:	9788132221845

Evolutionary Constrained Optimization
Evolutionary Constrained Optimization[electronic resource] /edited by Rituparna Datta, Kalyanmoy Deb. - 1st ed. 2015. - XVI, 319 p. 111 illus., 39 illus. in color.online resource. - Infosys Science Foundation Series in Applied Sciences and Engineering,2363-4995. - Infosys Science Foundation Series in Applied Sciences and Engineering,.

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.

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.

ISBN: 9788132221845

Standard No.: 10.1007/978-81-322-2184-5doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Evolutionary Constrained Optimization
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