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This book extends traditional evolutionary optimization algorithms in the contexts of noisy single and multi-objective optimization. It also demonstrates the scope of noisy optimization algorithms in real-world multi-agent coordination problems. Although many books, papers and conference proceedings are available on evolutionary optimization, there is still a scarcity of literature on noisy optimization, a gap that this book remedies. Unique in terms of its content, organization and above all insightful presentation of complex algorithms in an easy-to-read style, the book is primarily intended for graduate students and researchers in electrical engineering and computer science, though it will prove equally useful for researchers migrating from other disciplines. The book includes 7 chapters. Chapters 1 and 2 provide the prerequisites needed to understand the principles addressed in subsequent chapters. Chapter 3 offers a thorough review of the extant literature on noisy optimization, while Chapters 4 and 5 deal with single and multi-objective noisy evolutionary optimization, respectively. Chapter 6 aims at extending traditional optimization algorithms so as to enable them to work in the presence of stochastic noise. Lastly, Chapter 7 outlines the future scope of research and applications of noisy optimization.

This book extends traditional evolutionary optimization algorithms in the contexts of noisy single and multi-objective optimization. It also demonstrates the scope of noisy optimization algorithms in real-world multi-agent coordination problems. Although many books, papers and conference proceedings are available on evolutionary optimization, there is still a scarcity of literature on noisy optimization, a gap that this book remedies. Unique in terms of its content, organization and above all insightful presentation of complex algorithms in an easy-to-read style, the book is primarily intended for graduate students and researchers in electrical engineering and computer science, though it will prove equally useful for researchers migrating from other disciplines. The book includes 7 chapters. Chapters 1 and 2 provide the prerequisites needed to understand the principles addressed in subsequent chapters. Chapter 3 offers a thorough review of the extant literature on noisy optimization, while Chapters 4 and 5 deal with single and multi-objective noisy evolutionary optimization, respectively. Chapter 6 aims at extending traditional optimization algorithms so as to enable them to work in the presence of stochastic noise. Lastly, Chapter 7 outlines the future scope of research and applications of noisy optimization.