TY - BOOK ID - 7986384 TI - Multi-objective memetic algorithms. AU - Goh, Chi-Keong AU - Ong, Yew Soon AU - Tan, K. C. PY - 2009 SN - 354088050X 3540880518 PB - Berlin, Germany : Springer, DB - UniCat KW - Evolutionary computation KW - Genetic algorithms KW - Computer Science KW - Operations Research KW - Civil Engineering KW - Applied Mathematics KW - Civil & Environmental Engineering KW - Engineering & Applied Sciences KW - GAs (Algorithms) KW - Genetic searches (Algorithms) KW - Computation, Evolutionary KW - Engineering. KW - Artificial intelligence. KW - Applied mathematics. KW - Engineering mathematics. KW - Appl.Mathematics/Computational Methods of Engineering. KW - Artificial Intelligence (incl. Robotics). KW - Engineering KW - Engineering analysis KW - Mathematical analysis KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Construction KW - Industrial arts KW - Technology KW - Mathematics KW - Mathematical and Computational Engineering. KW - Artificial Intelligence. KW - Mehrkriterielle Optimierung KW - Genetic algorithms. KW - Evolutionary computation. KW - Memetischer Algorithmus. KW - Neural networks (Computer science) KW - Algorithms KW - Combinatorial optimization KW - Genetic programming (Computer science) KW - Learning classifier systems UR - https://www.unicat.be/uniCat?func=search&query=sysid:7986384 AB - 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, having demonstrated better efficacy in dealing with multi-objective problems as compared to its conventional counterparts. Nonetheless, researchers are only beginning to realize the vast potential of multi-objective Memetic algorithm and there remain many open topics in its design. This book presents a very first comprehensive collection of works, written by leading researchers in the field, and reflects the current state-of-the-art in the theory and practice of multi-objective Memetic algorithms. "Multi-Objective Memetic algorithms" is organized for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of Memetic algorithms and multi-objective optimization. ER -