Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial ApplicationsISBN: 978-0-471-99902-7
Hardcover
500 pages
July 1999
This is a Print-on-Demand title. It will be printed specifically to fill your order. Please allow an additional 10-15 days delivery time. The book is not returnable.
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Evolutionary Algorithms in Engineering and Computer Science Edited
by K. Miettinen, University of Jyväskylä, Finland M. M.
Mäkelä, University of Jyväskylä, Finland P.
Neittaanmäki, University of Jyväskylä, Finland J.
Périaux, 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.