Return to home page
Searching: Muskingum library catalog
Some OPAL libraries remain closed or are operating at reduced service levels. Materials from those libraries may not be requestable; requested items may take longer to arrive. Note that pickup procedures may differ between libraries. Please contact your library for new procedures, specific requests, or other assistance.
Record 4 of 12
  Previous Record Previous Item Next Item Next Record
  Reviews, Summaries, etc...
EBOOK
Title Evolutionary constrained optimization / Rituparna Datta, Kalyanmoy Deb, editors.
Imprint New Delhi : Springer, [2014]
2015

LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Series Infosys Science Foundation series, 2363-6149. Applied sciences and engineering, 2363-4995
Infosys Science Foundation series. Applied sciences and engineering. 2363-4995
Subject Constrained optimization.
Alt Name Datta, Rituparna,
Deb, Kalyanmoy,
LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Series Infosys Science Foundation series, 2363-6149. Applied sciences and engineering, 2363-4995
Infosys Science Foundation series. Applied sciences and engineering. 2363-4995
Subject Constrained optimization.
Alt Name Datta, Rituparna,
Deb, Kalyanmoy,
Description 1 online resource (xvi, 319 pages) : color illustrations.
polychrome rdacc
Note Includes index.
Contents Preface; Acknowledgments to Reviewers; Contents; About the Editors; 1 A Critical Review of Adaptive Penalty Techniques in Evolutionary Computation; 1.1 Introduction; 1.2 The Penalty Method; 1.3 A Taxonomy; 1.4 Some Adaptive Techniques; 1.4.1 The Early Years; 1.4.2 Using More Feedback; 1.4.3 Parameterless Techniques; 1.5 Related Techniques; 1.5.1 Self-adapting the Parameters; 1.5.2 Coevolving the Parameters; 1.5.3 Using Other Tools; 1.6 Discussion; 1.6.1 User-Defined Parameters; 1.6.2 Comparative Performance; 1.6.3 Implementation Issues; 1.6.4 Extensions; 1.7 Conclusion; References.
2 Ruggedness Quantifying for Constrained Continuous Fitness Landscapes2.1 Introduction; 2.2 Preliminaries; 2.2.1 Constrained Continuous Optimization Problem; 2.2.2 Fitness Landscape Ruggedness Analysis Using the Entropy Measure; 2.3 Ruggedness Quantification for Constrained Continuous Optimization; 2.3.1 Ruggedness Quantification; 2.3.2 Biased Sampling Using Evolution Strategies; 2.3.3 Dealing with Infeasible Areas; 2.3.4 Ruggedness Quantifying Method Using Constraint Handling Biased Walk; 2.4 Experimental Studies; 2.4.1 Constrained Sphere Function; 2.4.2 CEC Benchmark Problems.
2.5 ConclusionsReferences; 3 Trust Regions in Surrogate-Assisted Evolutionary Programming for Constrained Expensive Black-Box Optimization; 3.1 Introduction; 3.2 Review of Literature; 3.3 Trust Regions in Constrained Evolutionary Programming Using Surrogates; 3.3.1 Overview; 3.3.2 Algorithm Description; 3.3.3 Radial Basis Function Interpolation; 3.4 Numerical Experiments; 3.4.1 Benchmark Constrained Optimization Problems; 3.4.2 Alternative Methods; 3.4.3 Experimental Setup and Parameter Settings; 3.5 Results and Discussion; 3.5.1 Performance and Data Profiles.
3.5.2 Comparisons Between TRICEPS-RBF and CEP-RBF on the Benchmark Test Problems3.5.3 Comparisons Between TRICEPS-RBF and Alternative Methods on the Benchmark Test Problems; 3.5.4 Comparisons Between TRICEPS-RBF and Alternatives on the MOPTA08 Automotive Application Problem; 3.5.5 Sensitivity of TRICEPS-RBF to Algorithm Parameters; 3.6 Conclusions; References; 4 Ephemeral Resource Constraints in Optimization; 4.1 Introduction; 4.2 Ephemeral Resource-Constrained Optimization Problems (ERCOPs) in Overview; 4.2.1 Mathematical Formulation of ERCOPs; 4.2.2 Review of Basic ERCOP Properties.
4.3 ERCs in More Detail4.3.1 Commitment Relaxation ERCs; 4.3.2 Periodic ERCs; 4.3.3 Commitment Composite ERCs; 4.4 Theoretical Analysis of ERCs; 4.4.1 Markov Chains; 4.4.2 Modeling ERCs with Markov Models; 4.4.3 Simulation Results; 4.4.4 Summary of Theoretical Study; 4.5 Static Constraint-Handling Strategies; 4.5.1 Evaluation of Static Constraint-Handling Strategies; 4.6 Learning-Based Constraint-Handling Strategies; 4.6.1 Evaluation of Learning-Based Strategies; 4.7 Online Resource-Purchasing Strategies; 4.7.1 Evaluation of Online Resource-Purchasing Strategies; 4.8 Conclusion.
Summary 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.
Note Online resource; title from PDF title page (SpringerLink, viewed December 31, 2014).
ISBN 9788132221845 (electronic bk.)
8132221842 (electronic bk.)
9788132221838
OCLC # 898213733
Additional Format Print version: Datta, Rituparna. Evolutionary Constrained Optimization. New Delhi : Springer India, 2014 9788132221838


If you experience difficulty accessing or navigating this content, please contact the OPAL Support Team