Federated learning is a distributed machine approach to privacy preservation and two major technical challenges prevent wider application of federated learning. One that raises high demands on communication resources, since large number model parameters must be transmitted between the server clients. The other challenge training models such as deep neural networks in requires amount computation...

Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, trajectory networks (STNs), to model behavior MOEAs. Our approach uses idea decomposition, where problem transformed into several single-objective problems. We show that STNs can be used and distinguish two popular algorithms, MOEA/D NSGA-...

This chapter examines the possibilities and challenges that lie ahead for evolutionary music and art. Evolutionary computing methods have enabled new modes of creative expression in the art made by humans. One day, it may be possible for computers to make art autonomously. The idea of machines making art leads to the question: what do we mean by 'making art' and how do we recognise and acknowle...