Upcoming book: Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization
Nature-Inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. In this regard many different inspirational sources can be found for these solvers, such as the behavioral patterns of bats, fireflies, corals, bees or cuckoos, as well as the mechanisms behind genetic inheritance, musical harmony composition or bacterial foraging. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics to be covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, adaptive nature-inspired techniques, hyper heuristics, memetic methods, or distributed evolutionary techniques, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization, dynamic and constraint satisfaction problems. This material unleashes a great opportunity for researchers, lecturers, and practitioners interested in nature-inspired computation, artificial intelligence and optimization problems.
More information about the book can be found in its call for papers. Please, if you want to be informed about the upcoming updates, contact us!