This book considers two important forms of algorithm that are increasingly central in modern computing. Evolutionary algorithms use procedures inspired by the theory of evolution to solve problems and are an important interface between life sciences and engineering. Search algorithms are designed to find items with specified properties. Together, these approaches provide powerful problem-solving tools with varied applications in industrial and experimental contexts. The first section gives an overview of the advancing field of evolutionary algorithms. The first chapter gives a useful account of the development of evolutionary algorithms and their strengths and weaknesses. Subsequent chapters address topics such as the use of evolutionary algorithms for the optimization of microwave filters and for solving fuzzy-data problems. The second section discusses search algorithms, covering topics including population-based algorithms and search algorithms for engineering applications. The third section is specifically concerned with algorithms to solve optimization problems in engineering, such as in image processing, power engineering and multivariate data analysis. The range of material, from more introductory to more advanced, makes this book useful for students as well as for engineers and practitioners.
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