Advances in Evolutionary Algorithms: Theory, Design and Practice

ISBN-13: 9783540317586 / Angielski / Twarda / 2006 / 172 str.

Chang Wook Ahn; C. W. Ahn

Opis

Every real-world problem from economic to scientific and engineering fields is ultimately confronted with a common task, viz., optimization. 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.

Spis treści

Practical Genetic Algorithms.- Real-World Application: Routing Problem.- Elitist Compact Genetic Algorithms.- Real-coded Bayesian Optimization Algorithm.- Multiobjective Real-coded Bayesian Optimization Algorithm.- Conclusions.

Blurb

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.