Multi-Objective Memetic Algorithms

• Corporate Author: SpringerLink (Online service)
• Other Authors: Goh, Chi-Keong. (Editor), Ong, Yew-Soon. (Editor), Tan, Kay Chen. (Editor)
• Format: Electronic
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.
• Series: Studies in Computational Intelligence, 171
• Subjects: Engineering.; Artificial intelligence.; Engineering mathematics.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-540-88051-6

The application of sophisticated evolutionary computing approaches for solving complex problems with multiple conflicting objectives in science and engineering have increased steadily in the recent years. Within this growing trend, Memetic algorithms are, perhaps, one of the most successful stories,...

Table of Contents:

• Part I Introduction Evolutionary Multi-Multi-Objective Optimization  EMMOO
• Part II Knowledge Infused in Design of Problem-Specific Operators Solving Time-Tabling Problems using Evolutionary Algorithms and Heuristics Search
• Part III Knowledge Propagation through Cultural Evolution Risk and Cost Tradeoff In Economic Dispatch Including Wind Power Penetration Based on Multi-objective Memetic Particle Swarm Optimization
• Part IV Information Exploited for Local Improvement Combination of Genetic Algorithms and Evolution Strategies with Self-Adaptive Switching.