Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications

✍ Scribed by M. M. Makela, K. Miettinen, Pekka Neittaanmäki, M. M. Mäkelä, Jacques Périaux

• Publisher: Wiley
• Year: 1999
• Tongue: English
• Leaves: 500
• Edition: 1
• Category: Library

✦ Synopsis

Evolutionary Algorithms in Engineering and Computer Science Edited by K. Miettinen, University of Jyv?skyl?, Finland M. M. M?kel?, University of Jyv?skyl?, Finland P. Neittaanm?ki, University of Jyv?skyl?, Finland J. P?riaux, Dassault Aviation, France What is Evolutionary Computing? Based on the genetic message encoded in DNA, and digitalized algorithms inspired by the Darwinian framework of evolution by natural selection, Evolutionary Computing is one of the most important information technologies of our times. Evolutionary algorithms encompass all adaptive and computational models of natural evolutionary systems - genetic algorithms, evolution strategies, evolutionary programming and genetic programming. In addition, they work well in the search for global solutions to optimization problems, allowing the production of optimization software that is robust and easy to implement. Furthermore, these algorithms can easily be hybridized with traditional optimization techniques. This book presents state-of-the-art lectures delivered by international academic and industrial experts in the field of evolutionary computing. It bridges artificial intelligence and scientific computing with a particular emphasis on real-life problems encountered in application-oriented sectors, such as aerospace, electronics, telecommunications, energy and economics. This rapidly growing field, with its deep understanding and assesssment of complex problems in current practice, provides an effective, modern engineering tool. This book will therefore be of significant interest and value to all postgraduates, research scientists and practitioners facing complex optimization problems.