Evolutionary Machine Learning for Space

The Venn diagram of evolutionary computation, machine learning and space applications shows some intriguing overlaps. As evolutionary algorithms are often resource-intensive, they have not yet been applied in space. Nevertheless, it has been decisively demonstrated that evolutionary machine learning (EML) is a valuable tool for space, specifically in fields such as trajectory optimisation, optimal controlOptimal control and neuroevolutionNeuroevolution for robot control, where high-dimensional, discontinuous, sparse and/or non-linear problems abound. In the following chapter, we introduce common problems faced by the space research and application community, together with EML techniques used for generating robust, performant and, sometimes indeed, state-of-the-art solutions. The often complex mathematics behind some problems (especially in trajectory optimisation and optimal control)Optimal control has been simplified to the minimum necessary to convey the essence of the challenge without encumbering the overview of the relevant EML algorithms. We hope that this chapter provides useful information to both the EML and the space communities in the form of algorithms, benchmarks and standing challenges.