Ebook: Recent Advances in Memetic Algorithms
Author: W. E. Hart N. Krasnogor J. E. Smith (auth.) William E. Hart Dr. J. E. Smith N. Krasnogor (eds.)
- Tags: Appl.Mathematics/Computational Methods of Engineering, Artificial Intelligence (incl. Robotics)
- Series: Studies in Fuzziness and Soft Computing 166
- Year: 2005
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
Memetic algorithms are evolutionary algorithms that apply a local search process to refine solutions to hard problems. Memetic algorithms are the subject of intense scientific research and have been successfully applied to a multitude of real-world problems ranging from the construction of optimal university exam timetables, to the prediction of protein structures and the optimal design of space-craft trajectories. "Recent Advances in Memetic Algorithms" presents a rich state-of-the-art gallery of works on Memetic algorithms. Recent Advances in Memetic Algorithms is the first book that focuses on this technology as the central topical matter. This monograph gives a coherent, integrated view on both good practice examples and new trends including a concise and self-contained introduction to Memetic Algorithms. It is a necessary read for postgraduate students and researchers interested in recent advances in search and optimization technologies based on Memetic algorithms, but can also be used as complement to undergraduate textbooks on artificial intelligence.
Memetic algorithms are evolutionary algorithms that apply a local search process to refine solutions to hard problems. Memetic algorithms are the subject of intense scientific research and have been successfully applied to a multitude of real-world problems ranging from the construction of optimal university exam timetables, to the prediction of protein structures and the optimal design of space-craft trajectories. "Recent Advances in Memetic Algorithms" presents a rich state-of-the-art gallery of works on Memetic algorithms. Recent Advances in Memetic Algorithms is the first book that focuses on this technology as the central topical matter. This monograph gives a coherent, integrated view on both good practice examples and new trends including a concise and self-contained introduction to Memetic Algorithms. It is a necessary read for postgraduate students and researchers interested in recent advances in search and optimization technologies based on Memetic algorithms, but can also be used as complement to undergraduate textbooks on artificial intelligence.
Memetic algorithms are evolutionary algorithms that apply a local search process to refine solutions to hard problems. Memetic algorithms are the subject of intense scientific research and have been successfully applied to a multitude of real-world problems ranging from the construction of optimal university exam timetables, to the prediction of protein structures and the optimal design of space-craft trajectories. "Recent Advances in Memetic Algorithms" presents a rich state-of-the-art gallery of works on Memetic algorithms. Recent Advances in Memetic Algorithms is the first book that focuses on this technology as the central topical matter. This monograph gives a coherent, integrated view on both good practice examples and new trends including a concise and self-contained introduction to Memetic Algorithms. It is a necessary read for postgraduate students and researchers interested in recent advances in search and optimization technologies based on Memetic algorithms, but can also be used as complement to undergraduate textbooks on artificial intelligence.
Content:
Front Matter....Pages I-X
Front Matter....Pages 1-1
Memetic Evolutionary Algorithms....Pages 3-27
Front Matter....Pages 29-29
An Evolutionary Approach for the Maximum Diversity Problem....Pages 31-47
Multimeme Algorithms Using Fuzzy Logic Based Memes For Protein Structure Prediction....Pages 49-64
A Memetic Algorithm Solving the VRP, the CARP and General Routing Problems with Nodes, Edges and Arcs....Pages 65-85
Using Memetic Algorithms for Optimal Calibration of Automotive Internal Combustion Engines....Pages 87-104
The Co-Evolution of Memetic Algorithms for Protein Structure Prediction....Pages 105-128
Hybrid Evolutionary Approaches to Terminal Assignment in Communications Networks....Pages 129-159
Effective Exploration & Exploitation of the Solution Space via Memetic Algorithms for the Circuit Partition Problem....Pages 161-182
Front Matter....Pages 183-183
Towards Robust Memetic Algorithms....Pages 185-207
NK-Fitness Landscapes and Memetic Algorithms with Greedy Operators and k-opt Local Search....Pages 209-228
Self-Assembling of Local Searchers in Memetic Algorithms....Pages 229-257
Designing Efficient Genetic and Evolutionary Algorithm Hybrids....Pages 259-288
The Design of Memetic Algorithms for Scheduling and Timetabling Problems....Pages 289-311
Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects....Pages 313-352
Front Matter....Pages 353-353
A Memetic Learning Classifier System for Describing Continuous-Valued Problem Spaces....Pages 355-395
Angels & Mortals: A New Combinatorial Optimization Algorithm....Pages 397-405
Back Matter....Pages 407-408
Memetic algorithms are evolutionary algorithms that apply a local search process to refine solutions to hard problems. Memetic algorithms are the subject of intense scientific research and have been successfully applied to a multitude of real-world problems ranging from the construction of optimal university exam timetables, to the prediction of protein structures and the optimal design of space-craft trajectories. "Recent Advances in Memetic Algorithms" presents a rich state-of-the-art gallery of works on Memetic algorithms. Recent Advances in Memetic Algorithms is the first book that focuses on this technology as the central topical matter. This monograph gives a coherent, integrated view on both good practice examples and new trends including a concise and self-contained introduction to Memetic Algorithms. It is a necessary read for postgraduate students and researchers interested in recent advances in search and optimization technologies based on Memetic algorithms, but can also be used as complement to undergraduate textbooks on artificial intelligence.
Content:
Front Matter....Pages I-X
Front Matter....Pages 1-1
Memetic Evolutionary Algorithms....Pages 3-27
Front Matter....Pages 29-29
An Evolutionary Approach for the Maximum Diversity Problem....Pages 31-47
Multimeme Algorithms Using Fuzzy Logic Based Memes For Protein Structure Prediction....Pages 49-64
A Memetic Algorithm Solving the VRP, the CARP and General Routing Problems with Nodes, Edges and Arcs....Pages 65-85
Using Memetic Algorithms for Optimal Calibration of Automotive Internal Combustion Engines....Pages 87-104
The Co-Evolution of Memetic Algorithms for Protein Structure Prediction....Pages 105-128
Hybrid Evolutionary Approaches to Terminal Assignment in Communications Networks....Pages 129-159
Effective Exploration & Exploitation of the Solution Space via Memetic Algorithms for the Circuit Partition Problem....Pages 161-182
Front Matter....Pages 183-183
Towards Robust Memetic Algorithms....Pages 185-207
NK-Fitness Landscapes and Memetic Algorithms with Greedy Operators and k-opt Local Search....Pages 209-228
Self-Assembling of Local Searchers in Memetic Algorithms....Pages 229-257
Designing Efficient Genetic and Evolutionary Algorithm Hybrids....Pages 259-288
The Design of Memetic Algorithms for Scheduling and Timetabling Problems....Pages 289-311
Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects....Pages 313-352
Front Matter....Pages 353-353
A Memetic Learning Classifier System for Describing Continuous-Valued Problem Spaces....Pages 355-395
Angels & Mortals: A New Combinatorial Optimization Algorithm....Pages 397-405
Back Matter....Pages 407-408
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