Evolutionary Algorithms (Volume 9)

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【簡介】 Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning. 【目錄】

EVOLUTIONARY ALGORITHMS IN ENGINEERING AND COMPUTER SSCIENCE

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DESCRIPTION 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.