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This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time.

Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.




This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time.

Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.




This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time.

Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.


Content:
Front Matter....Pages 1-24
Front Matter....Pages 1-1
Evolutionary Dynamic Optimization: Test and Evaluation Environments....Pages 3-37
Evolutionary Dynamic Optimization: Methodologies....Pages 39-64
Evolutionary Dynamic Optimization: Challenges and Perspectives....Pages 65-84
Dynamic Multi-objective Optimization: A Survey of the State-of-the-Art....Pages 85-106
Front Matter....Pages 107-107
A Comparative Study on Particle Swarm Optimization in Dynamic Environments....Pages 109-136
Memetic Algorithms for Dynamic Optimization Problems....Pages 137-170
BIPOP: A New Algorithm with Explicit Exploration/Exploitation Control for Dynamic Optimization Problems....Pages 171-191
Evolutionary Optimization on Continuous Dynamic Constrained Problems - An Analysis....Pages 193-217
Front Matter....Pages 219-219
Theoretical Advances in Evolutionary Dynamic Optimization....Pages 221-240
Analyzing Evolutionary Algorithms for Dynamic Optimization Problems Based on the Dynamical Systems Approach....Pages 241-267
Dynamic Fitness Landscape Analysis....Pages 269-297
Dynamics in the Multi-objective Subset Sum: Analysing the Behavior of Population Based Algorithms....Pages 299-313
Front Matter....Pages 315-315
Ant Colony Optimization Algorithms with Immigrants Schemes for the Dynamic Travelling Salesman Problem....Pages 317-341
Genetic Algorithms for Dynamic Routing Problems in Mobile Ad Hoc Networks....Pages 343-375
Evolutionary Computation for Dynamic Capacitated Arc Routing Problem....Pages 377-401
Evolutionary Algorithms for the Multiple Unmanned Aerial Combat Vehicles Anti-ground Attack Problem in Dynamic Environments....Pages 403-431
Advanced Planning in Vertically Integrated Wine Supply Chains....Pages 433-463
Back Matter....Pages 465-469


This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time.

Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.


Content:
Front Matter....Pages 1-24
Front Matter....Pages 1-1
Evolutionary Dynamic Optimization: Test and Evaluation Environments....Pages 3-37
Evolutionary Dynamic Optimization: Methodologies....Pages 39-64
Evolutionary Dynamic Optimization: Challenges and Perspectives....Pages 65-84
Dynamic Multi-objective Optimization: A Survey of the State-of-the-Art....Pages 85-106
Front Matter....Pages 107-107
A Comparative Study on Particle Swarm Optimization in Dynamic Environments....Pages 109-136
Memetic Algorithms for Dynamic Optimization Problems....Pages 137-170
BIPOP: A New Algorithm with Explicit Exploration/Exploitation Control for Dynamic Optimization Problems....Pages 171-191
Evolutionary Optimization on Continuous Dynamic Constrained Problems - An Analysis....Pages 193-217
Front Matter....Pages 219-219
Theoretical Advances in Evolutionary Dynamic Optimization....Pages 221-240
Analyzing Evolutionary Algorithms for Dynamic Optimization Problems Based on the Dynamical Systems Approach....Pages 241-267
Dynamic Fitness Landscape Analysis....Pages 269-297
Dynamics in the Multi-objective Subset Sum: Analysing the Behavior of Population Based Algorithms....Pages 299-313
Front Matter....Pages 315-315
Ant Colony Optimization Algorithms with Immigrants Schemes for the Dynamic Travelling Salesman Problem....Pages 317-341
Genetic Algorithms for Dynamic Routing Problems in Mobile Ad Hoc Networks....Pages 343-375
Evolutionary Computation for Dynamic Capacitated Arc Routing Problem....Pages 377-401
Evolutionary Algorithms for the Multiple Unmanned Aerial Combat Vehicles Anti-ground Attack Problem in Dynamic Environments....Pages 403-431
Advanced Planning in Vertically Integrated Wine Supply Chains....Pages 433-463
Back Matter....Pages 465-469
....
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