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Advances in Evolutionary Algorithms [electronic resource] :Theory, Design and Practice / by Chang Wook Ahn.

by Ahn, Chang Wook [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 18Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.Description: XV, 171 p. Also available online. online resource.ISBN: 9783540317593.Subject(s): Engineering | Artificial intelligence | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)DDC classification: 519 Online resources: Click here to access online
Contents:
Practical Genetic Algorithms -- Real-World Application: Routing Problem -- Elitist Compact Genetic Algorithms -- Real-coded Bayesian Optimization Algorithm -- Multiobjective Real-coded Bayesian Optimization Algorithm -- Conclusions.
In: Springer eBooksSummary: Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. The goal of this book is to provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms. In this regard, five significant issues have been investigated: Bridging the gap between theory and practice of GEAs, thereby providing practical design guidelines. Demonstrating the practical use of the suggested road map. Offering a useful tool to significantly enhance the exploratory power in time-constrained and memory-limited applications. Providing a class of promising procedures that are capable of scalably solving hard problems in the continuous domain. Opening an important track for multiobjective GEA research that relies on decomposition principle. This book serves to play a decisive role in bringing forth a paradigm shift in future evolutionary computation.
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Practical Genetic Algorithms -- Real-World Application: Routing Problem -- Elitist Compact Genetic Algorithms -- Real-coded Bayesian Optimization Algorithm -- Multiobjective Real-coded Bayesian Optimization Algorithm -- Conclusions.

Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. The goal of this book is to provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms. In this regard, five significant issues have been investigated: Bridging the gap between theory and practice of GEAs, thereby providing practical design guidelines. Demonstrating the practical use of the suggested road map. Offering a useful tool to significantly enhance the exploratory power in time-constrained and memory-limited applications. Providing a class of promising procedures that are capable of scalably solving hard problems in the continuous domain. Opening an important track for multiobjective GEA research that relies on decomposition principle. This book serves to play a decisive role in bringing forth a paradigm shift in future evolutionary computation.

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