Intended for researchers and practitioners alike, this book covers
carefully selected yet broad topics in optimization, machine learning,
and metaheuristics. Written by world-leading academic researchers who
are extremely experienced in industrial applications, this
self-contained book is the first of its kind that provides comprehensive
background knowledge, particularly practical guidelines, and
state-of-the-art techniques. New algorithms are carefully explained,
further elaborated with pseudocode or flowcharts, and full working
source code is made freely available.
This is followed by a presentation of a variety of data-driven single-
and multi-objective optimization algorithms that seamlessly integrate
modern machine learning such as deep learning and transfer learning with
evolutionary and swarm optimization algorithms. Applications of
data-driven optimization ranging from aerodynamic design, optimization
of industrial processes, to deep neural architecture search are
included.