Authors:
There are no directly competing books that combine modern statistical approaches and evolutionary computing approaches in the design of experiments and the solution to optimization problems, and the author is very well positioned to address this gap given his background in industrial research on evolutionary computing applications, a corresponding academic research position, and corresponding teaching experience.
He has run the tutorial directly relevant to this topic at the main conference in the field, and will do so again in 2006.
Includes supplementary material: sn.pub/extras
Part of the book series: Natural Computing Series (NCS)
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Table of contents (9 chapters)
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Front Matter
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Results and Perspectives
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Front Matter
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Back Matter
About this book
Experimentation is necessary - a purely theoretical approach is not reasonable. The new experimentalism, a development in the modern philosophy of science, considers that an experiment can have a life of its own. It provides a statistical methodology to learn from experiments, where the experimenter should distinguish between statistical significance and scientific meaning.
This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. The book develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. Treating optimization runs as experiments, the author offers methods for solving complex real-world problems that involve optimization via simulation, and he describes successful applications in engineering and industrial control projects.
The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples, so it is suitable for practitioners and researchers and also for lecturers and students. It summarizes results from the author's consulting to industry and his experience teaching university courses and conducting tutorials at international conferences. The book will be supported online with downloads and exercises.
Keywords
- Performance
- algorithm
- algorithms
- evolution
- evolutionary algorithm
- evolutionary computation
- heuristics
- optimization
- simulation
- statistics
Authors and Affiliations
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Department of Computer Science, University of Dortmund, Dortmund, Germany
Thomas Bartz-Beielstein
Bibliographic Information
Book Title: Experimental Research in Evolutionary Computation
Book Subtitle: The New Experimentalism
Authors: Thomas Bartz-Beielstein
Series Title: Natural Computing Series
DOI: https://doi.org/10.1007/3-540-32027-X
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2006
Hardcover ISBN: 978-3-540-32026-5Published: 20 March 2006
Softcover ISBN: 978-3-642-06873-7Published: 25 November 2010
eBook ISBN: 978-3-540-32027-2Published: 09 May 2006
Series ISSN: 1619-7127
Series E-ISSN: 2627-6461
Edition Number: 1
Number of Pages: XIV, 215
Topics: Artificial Intelligence, Theory of Computation, Simulation and Modeling, Computer Applications, Optimization, Mathematical and Computational Engineering