Theory and Principled Methods for the Design of Metaheuristics

Metaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex.

In this book the editors establish a bridge between theory and practice, presenting principled methods that incorporate problem knowledge in evolutionary algorithms and other metaheuristics. The book consists of 11 chapters dealing with the following topics: theoretical results that show what is not possible, an assessment of unsuccessful lines of empirical research; methods for rigorously defining the appropriate scope of problems while acknowledging the compromise between the class of problems to which a search algorithm is applied and its overall expected performance; the top-down principled design of search algorithms, in particular showing that it is possible to design algorithms that are provably good for some rigorously defined classes; and, finally, principled practice, that is reasoned and systematic approaches to setting up experiments, metaheuristic adaptation to specific problems, and setting parameters.

With contributions by some of the leading researchers in this domain, this book will be of significant value to scientists, practitioners, and graduate students in the areas of evolutionary computing, metaheuristics, and computational intelligence.

• Evolutionary algorithms
• Evolutionary computing
• Global optimization
• Heuristics
• Landscapes
• Metaheuristics
• Multiobjective optimization
• Representations
• Search algorithms

From the book reviews:

“This is a valuable addition to the literature on heuristics for search. Both practitioners and theoreticians should read it.” (J. P. E. Hodgson, Computing Reviews, July, 2014)

• VisualDNA, London, United Kingdom

  Yossi Borenstein

• University of Birmingham School of Computer Science, Birmingham, United Kingdom

  Alberto Moraglio

Dr. Yossi Borenstein is the head of risk analytics at the company VisualDNA; he previously held a position at the University of Hertfordshire, and he received his PhD from the University of Essex; his research interests include data analysis, information retrieval, stochastic optimization, artificial intelligence, and evolutionary computation.

Dr. Alberto Moraglio is a lecturer in the Dept. of Computer Science of the University of Exeter. He previously held positions at the University of Birmingham and the University of Coimbra, and he received his PhD from the University of Essex. His research focus is the theory of evolutionary computation.

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