Recent Advances in Memetic Algorithms [electronic resource] / edited by William E. Hart, J. E. Smith, N. Krasnogor.Material type: TextLanguage: English Series: Studies in Fuzziness and Soft Computing: 166Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005Description: X, 410 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540323631Subject(s): Engineering | Artificial intelligence | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 519 LOC classification: TA329-348TA640-643Online resources: Click here to access online
to Memetic Algorithms -- Memetic Evolutionary Algorithms -- Applications of Memetic Algorithms -- An Evolutionary Approach for the Maximum Diversity Problem -- Multimeme Algorithms Using Fuzzy Logic Based Memes For Protein Structure Prediction -- A Memetic Algorithm Solving the VRP, the CARP and General Routing Problems with Nodes, Edges and Arcs -- Using Memetic Algorithms for Optimal Calibration of Automotive Internal Combustion Engines -- The Co-Evolution of Memetic Algorithms for Protein Structure Prediction -- Hybrid Evolutionary Approaches to Terminal Assignment in Communications Networks -- Effective Exploration & Exploitation of the Solution Space via Memetic Algorithms for the Circuit Partition Problem -- Methodological Aspects of Memetic Algorithms -- Towards Robust Memetic Algorithms -- NK-Fitness Landscapes and Memetic Algorithms with Greedy Operators and k-opt Local Search -- Self-Assembling of Local Searchers in Memetic Algorithms -- Designing Efficient Genetic and Evolutionary Algorithm Hybrids -- The Design of Memetic Algorithms for Scheduling and Timetabling Problems -- Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects -- Related Search Technologies -- A Memetic Learning Classifier System for Describing Continuous-Valued Problem Spaces -- Angels & Mortals: A New Combinatorial Optimization Algorithm.
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.