Ebook: Advances in Evolutionary Algorithms: Theory, Design and Practice

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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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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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.

Content:
Front Matter....Pages I-XV
Introduction....Pages 1-5
Practical Genetic Algorithms....Pages 7-22
Real-World Application: Routing Problem....Pages 23-43
Elitist Compact Genetic Algorithms....Pages 45-83
Real-coded Bayesian Optimization Algorithm....Pages 85-124
Multiobjective Real-coded Bayesian Optimization Algorithm....Pages 125-151
Conclusions....Pages 153-157
Back Matter....Pages 159-171

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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.

Content:
Front Matter....Pages I-XV
Introduction....Pages 1-5
Practical Genetic Algorithms....Pages 7-22
Real-World Application: Routing Problem....Pages 23-43
Elitist Compact Genetic Algorithms....Pages 45-83
Real-coded Bayesian Optimization Algorithm....Pages 85-124
Multiobjective Real-coded Bayesian Optimization Algorithm....Pages 125-151
Conclusions....Pages 153-157
Back Matter....Pages 159-171
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