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Table of 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
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