The field of metaheuristics for the application to combinatorial optimization problems is a
rapidly growing field of research. This is due to the importance of combinatorial optimization …

Classification is one of the most researched questions in machine learning and data mining.
A wide range of real problems have been stated as classification problems, for example …

Abstract Deep Reinforcement Learning (DRL) algorithms have been successfully applied to
a range of challenging control tasks. However, these methods typically suffer from three core …

Multiobjective evolutionary algorithms (MOEAs) have gained much attention due to their
high effectiveness and efficiency in solving multiobjective optimization problems (MOPs) …

From the Publisher: With the publication of this book, Hans-Paul Schwefel has responded to
rapidly growing interest in Evolutionary Computation, a field that originated, in part, with his …

This invaluable book has been designed to be useful to most practising scientists and
engineers, whatever their field and however rusty their mathematics and programming might …

Self-adaptive software requires high dependability robustness, adaptability, and availability.
The article describes an infrastructure supporting two simultaneous processes in self …

Deep reinforcement learning algorithms have been successfully applied to a range of
challenging control tasks. However, these methods typically struggle with achieving effective …

The latest update to Bela Liptak's acclaimed" bible" of instrument engineering is now
available. Retaining the format that made the previous editions bestsellers in their own right …

We introduce a framework, called “physicomimetics,” that provides distributed control of
large collections of mobile physical agents in sensor networks. The agents sense and react …