Evolutionary Computation for Dynamic Optimization Problems

✍ Scribed by Shengxiang Yang, Trung Thanh Nguyen, Changhe Li (auth.), Shengxiang Yang, Xin Yao (eds.)

Publisher: Springer-Verlag Berlin Heidelberg
Year: 2013
Tongue: English
Leaves: 479
Series: Studies in Computational Intelligence 490
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

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

✦ Subjects

Computational Intelligence; Artificial Intelligence (incl. Robotics)

📜 SIMILAR VOLUMES

Evolutionary computation for modeling an

📁 Evolutionary computation for modeling and optimization

✍ Daniel Ashlock 📂 Library 📅 2006 🏛 Springer 🌐 English

Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introd

Evolutionary Computation for Modeling an

📁 Evolutionary Computation for Modeling and Optimization

✍ Daniel Ashlock (auth.) 📂 Library 📅 2006 🏛 Springer-Verlag New York 🌐 English

Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It int

Evolutionary Computation for Modeling an

📁 Evolutionary Computation for Modeling and Optimization

✍ Daniel Ashlock 📂 Library 📅 2006 🏛 Springer 🌐 English

Evolutionary computation includes Genetic Algorithms, Evolutionary Programming, Evolution Strategies, and Genetic Programming. In general any population based, selectionist algorithm that performs optimization or supports modeling is a form of evolutionary computation. This text covers primarily gen

Evolutionary Optimization in Dynamic Env

📁 Evolutionary Optimization in Dynamic Environments

✍ Jürgen Branke (auth.) 📂 Library 📅 2002 🏛 Springer US 🌐 English

Evolutionary Algorithms (EAs) have grown into a mature field of research in optimization, and have proven to be effective and robust problem solvers for a broad range of static real-world optimization problems. Yet, since they are based on the principles of natural evolution, and since natural ev

Recent Advances in Evolutionary Computat

📁 Recent Advances in Evolutionary Computation for Combinatorial Optimization

✍ Matthew J. Craven (auth.), Carlos Cotta, Jano van Hemert (eds.) 📂 Library 📅 2008 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

Combinatorial optimisation is a ubiquitous discipline whose usefulness spans vast applications domains. The intrinsic complexity of most combinatorial optimisation problems makes classical methods unaffordable in many cases. To acquire practical solutions to these problems requires the use of

Evolutionary Algorithms for Solving Mult

📁 Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)

✍ Carlos Coello Coello, Gary B. Lamont, David A. van Veldhuizen 📂 Library 📅 2007 🏛 Springer 🌐 English

This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorpor