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000724886 24500 $$aEvolutionary constrained optimization$$h[electronic resource] /$$cRituparna Datta, Kalyanmoy Deb, editors.
000724886 264_1 $$aNew Delhi :$$bSpringer,$$c[2014]
000724886 264_4 $$c©2015
000724886 300__ $$a1 online resource (xvi, 319 pages) :$$bcolor illustrations.
000724886 336__ $$atext$$btxt$$2rdacontent
000724886 337__ $$acomputer$$bc$$2rdamedia
000724886 338__ $$aonline resource$$bcr$$2rdacarrier
000724886 4901_ $$aInfosys Science Foundation series,$$x2363-6149.
000724886 500__ $$aIncludes index.
000724886 5050_ $$aPreface; 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
000724886 5058_ $$a2 Ruggedness Quantifying for Constrained Continuous Fitness Landscapes2.1 Introduction; 2.2 Preliminaries; 2.2.1 Constrained Continuous Optimization Problem; 2.2.2 Fitness Landscape Ruggedness Analysis Using the Entropy Measure; 2.3 Ruggedness Quantification for Constrained Continuous Optimization; 2.3.1 Ruggedness Quantification; 2.3.2 Biased Sampling Using Evolution Strategies; 2.3.3 Dealing with Infeasible Areas; 2.3.4 Ruggedness Quantifying Method Using Constraint Handling Biased Walk; 2.4 Experimental Studies; 2.4.1 Constrained Sphere Function; 2.4.2 CEC Benchmark Problems
000724886 5058_ $$a2.5 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
000724886 5058_ $$a3.5.2 Comparisons Between TRICEPS-RBF and CEP-RBF on the Benchmark Test Problems3.5.3 Comparisons Between TRICEPS-RBF and Alternative Methods on the Benchmark Test Problems; 3.5.4 Comparisons Between TRICEPS-RBF and Alternatives on the MOPTA08 Automotive Application Problem; 3.5.5 Sensitivity of TRICEPS-RBF to Algorithm Parameters; 3.6 Conclusions; References; 4 Ephemeral Resource Constraints in Optimization; 4.1 Introduction; 4.2 Ephemeral Resource-Constrained Optimization Problems (ERCOPs) in Overview; 4.2.1 Mathematical Formulation of ERCOPs; 4.2.2 Review of Basic ERCOP Properties
000724886 5058_ $$a4.3 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
000724886 506__ $$aAccess limited to authorized users.
000724886 520__ $$aThis 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;
000724886 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 31, 2014).
000724886 650_0 $$aConstrained optimization.
000724886 7001_ $$aDatta, Rituparna,$$eeditor.
000724886 7001_ $$aDeb, Kalyanmoy,$$eeditor.
000724886 77608 $$iPrint version:$$aDatta, Rituparna$$tEvolutionary Constrained Optimization$$dNew Delhi : Springer India,c2014$$z9788132221838
000724886 830_0 $$aInfosys Science Foundation series.$$pApplied sciences and engineering.
000724886 852__ $$bebk
000724886 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-81-322-2184-5$$zOnline Access$$91397441.1
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000724886 980__ $$aBIB
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