During the past few years, the popularity of evolutionary algorithms (EAs) has skyrocketed at a rate that shows no signs of slowing down. One of the most notable recent additions to the EA family is biogeography-based optimization (BBO), which is based on mathematical models of the migration, speciation, and extinction of biological organisms.
This book, co-authored by the inventor of BBO and by one of its earliest and most significant contributors, presents BBO by first presenting its scientific and mathematical foundations. In addition to presenting the basic ideas of BBO in an easily understandable form, this work includes advanced material, such as mathematical models of BBO using Markov models, dynamic system models, and statistical mechanics models. The extension of BBO to noisy problems, combinatorial problems, constrained problems, and multi-objective problems is also covered.
This book will be an important tool for researchers who want to learn how to apply BBO and other EAs to these special types of problems.
1. The Science of Biogeography.
2. Biogeography and Biological Optimization.
3. A Basic BBO Algorithm
4. BBO Extensions.
5. BBO as a Markov Process.
6. Dynamic System Models of BBO.
7. Statistical Mechanics Approximations of BBO.
8. BBO for Combinatorial Optimization.
9. Constrained BBO.
10. BBO in Noisy Environments.
11. Multi-objective BBO.
12. Hybrid BBO Algorithms.
Haiping Ma is Associate Professor at Shaoxing University, China. His research interests include computer intelligence, signal processing, information fusion, and related subjects.
Dan Simon is Professor and Associate Vice President for Research at Cleveland State University, USA. His research interests include control theory, computer intelligence, embedded systems, and related subjects. He is the author of the text Evolutionary Optimization Algorithms.
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