Ebook: Evolutionary Computation in Dynamic and Uncertain Environments

This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The motivation for this book arises from the fact that some degree of uncertainty in characterizing any realistic engineering systems is inevitable. Representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums, are presented. "Evolutionary Computation in Dynamic and Uncertain Environments" is a valuable reference for scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, natural computing and evolutionary computation.

---

This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The motivation for this book arises from the fact that some degree of uncertainty in characterizing any realistic engineering systems is inevitable. Representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums, are presented. "Evolutionary Computation in Dynamic and Uncertain Environments" is a valuable reference for scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, natural computing and evolutionary computation.

---

This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The motivation for this book arises from the fact that some degree of uncertainty in characterizing any realistic engineering systems is inevitable. Representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums, are presented. "Evolutionary Computation in Dynamic and Uncertain Environments" is a valuable reference for scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, natural computing and evolutionary computation.

Content:
Front Matter....Pages I-XXIII
Front Matter....Pages 1-1
Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments....Pages 3-28
Particle Swarm Optimization in Dynamic Environments....Pages 29-49
Evolution Strategies in Dynamic Environments....Pages 51-77
Orthogonal Dynamic Hill Climbing Algorithm: ODHC....Pages 79-104
Genetic Algorithms with Self-Organizing Behaviour in Dynamic Environments....Pages 105-127
Learning and Anticipation in Online Dynamic Optimization....Pages 129-152
Evolutionary Online Data Mining: An Investigation in a Dynamic Environment....Pages 153-178
Adaptive Business Intelligence: Three Case Studies....Pages 179-196
Evolutionary Algorithms for Combinatorial Problems in the Uncertain Environment of the Wireless Sensor Networks....Pages 197-222
Front Matter....Pages 224-224
Individual-based Management of Meta-models for Evolutionary Optimization with Application to Three-Dimensional Blade Optimization....Pages 225-250
Evolutionary Shape Optimization Using Gaussian Processes....Pages 251-267
A Study of Techniques to Improve the Efficiency of a Multi-Objective Particle Swarm Optimizer....Pages 269-296
An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks....Pages 297-322
Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design....Pages 323-342
Front Matter....Pages 344-344
Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation....Pages 345-369
Evolving Multi Rover Systems in Dynamic and Noisy Environments....Pages 371-387
A Memetic Algorithm Using a Trust-Region Derivative-Free Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Black-box Functions....Pages 389-415
Genetic Algorithm to Optimize Fitness Function with Sampling Error and its Application to Financial Optimization Problem....Pages 417-434
Front Matter....Pages 436-436
Single/Multi-objective Inverse Robust Evolutionary Design Methodology in the Presence of Uncertainty....Pages 437-456
Evolving the Tradeoffs between Pareto-Optimality and Robustness in Multi-Objective Evolutionary Algorithms....Pages 457-478
Front Matter....Pages 436-436
Evolutionary Robust Design of Analog Filters Using Genetic Programming....Pages 479-496
Robust Salting Route Optimization Using Evolutionary Algorithms....Pages 497-517
An Evolutionary Approach For Robust Layout Synthesis of MEMS....Pages 519-542
A Hybrid Approach Based on Evolutionary Strategies and Interval Arithmetic to Perform Robust Designs....Pages 543-564
An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to Multi-Objective Models....Pages 565-582
Deterministic Robust Optimal Design Based on Standard Crowding Genetic Algorithm....Pages 583-598
Back Matter....Pages 599-606

---

This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The motivation for this book arises from the fact that some degree of uncertainty in characterizing any realistic engineering systems is inevitable. Representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums, are presented. "Evolutionary Computation in Dynamic and Uncertain Environments" is a valuable reference for scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, natural computing and evolutionary computation.

Content:
Front Matter....Pages I-XXIII
Front Matter....Pages 1-1
Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments....Pages 3-28
Particle Swarm Optimization in Dynamic Environments....Pages 29-49
Evolution Strategies in Dynamic Environments....Pages 51-77
Orthogonal Dynamic Hill Climbing Algorithm: ODHC....Pages 79-104
Genetic Algorithms with Self-Organizing Behaviour in Dynamic Environments....Pages 105-127
Learning and Anticipation in Online Dynamic Optimization....Pages 129-152
Evolutionary Online Data Mining: An Investigation in a Dynamic Environment....Pages 153-178
Adaptive Business Intelligence: Three Case Studies....Pages 179-196
Evolutionary Algorithms for Combinatorial Problems in the Uncertain Environment of the Wireless Sensor Networks....Pages 197-222
Front Matter....Pages 224-224
Individual-based Management of Meta-models for Evolutionary Optimization with Application to Three-Dimensional Blade Optimization....Pages 225-250
Evolutionary Shape Optimization Using Gaussian Processes....Pages 251-267
A Study of Techniques to Improve the Efficiency of a Multi-Objective Particle Swarm Optimizer....Pages 269-296
An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks....Pages 297-322
Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design....Pages 323-342
Front Matter....Pages 344-344
Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation....Pages 345-369
Evolving Multi Rover Systems in Dynamic and Noisy Environments....Pages 371-387
A Memetic Algorithm Using a Trust-Region Derivative-Free Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Black-box Functions....Pages 389-415
Genetic Algorithm to Optimize Fitness Function with Sampling Error and its Application to Financial Optimization Problem....Pages 417-434
Front Matter....Pages 436-436
Single/Multi-objective Inverse Robust Evolutionary Design Methodology in the Presence of Uncertainty....Pages 437-456
Evolving the Tradeoffs between Pareto-Optimality and Robustness in Multi-Objective Evolutionary Algorithms....Pages 457-478
Front Matter....Pages 436-436
Evolutionary Robust Design of Analog Filters Using Genetic Programming....Pages 479-496
Robust Salting Route Optimization Using Evolutionary Algorithms....Pages 497-517
An Evolutionary Approach For Robust Layout Synthesis of MEMS....Pages 519-542
A Hybrid Approach Based on Evolutionary Strategies and Interval Arithmetic to Perform Robust Designs....Pages 543-564
An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to Multi-Objective Models....Pages 565-582
Deterministic Robust Optimal Design Based on Standard Crowding Genetic Algorithm....Pages 583-598
Back Matter....Pages 599-606
....