The origins of this book date back to 2014, during a period when one of the authors (TT) was working in the other’s (AD’s) lab at Monash University in Australia. We had first met at a conference on Artificial Life in 2002; our shared interests in the subject meant that we had kept in touch regularly since then, despite living on opposite sides of the world (TT in Edinburgh, Scotland, and AD in Melbourne, Australia).

Artificial Life (or “ALife” for short) is the application of biological design principles for building complex, intelligent systems. It might be studied in software, hardware or “wetware” (molecular systems), and might be used for a variety of purposes. The two most common reasons people pursue ALife are as an approach to understanding biological systems and as an approach to building intelligent robots and artificial intelligence (AI) systems. It is also used by philosophers, social scientists, artists, and many others besides.

The ALife conferences are an exciting interdisciplinary melting-pot of ideas. At the conference in 2002, we soon discovered that we shared very similar interests in designing artificial worlds in software. We both used computational analogues to the processes of biological self-reproduction, evolution and natural selection to populate our worlds with interesting creatures. We had both completed PhDs in this area in the 1990s. Beyond our experimental work, we also shared an interest in early mechanical models of living systems, and in the history of thought about technology inspired by biology.

During our period of working together in 2014, our original inspiration for writing this review came from reading a recently published account (Bullock, 2008) of Alfred Marshall’s ideas of machine self-reproduction and evolution from the 1860s (we discuss his work in Sect. 3.2). Although we were aware of Samuel Butler’s writing on the subject at around the same time (Sect. 3.1), we had not come across Marshall in this context before. We were therefore curious whether there might be other work from around this time, or even earlier, that discussed such ideas.

Another motivation was to highlight the pioneering work in the early 1950s of Nils Aall Barricelli on self-reproduction and evolution in software (Sect. 5.2.1). While John von Neumann’s theoretical work on self-reproducing machines from around that time is widely discussed in the literature (Sect. 5.1.1), personal experience suggested that Barricelli was still a relatively unknown figure within the ALife community, despite having a strong claim to be regarded as one of the field’s founding fathers.

Our purpose in writing this book was therefore to review the early history of the idea of self-reproducing and evolving machines, tracing it back as far as we could. This being the case, much of the book (Chaps. 26) is written as a guide to the literature on the subject, presented in chronological order from the earliest inklings of the idea up to the present day. While we provide some commentary and suggest classifications of the work in terms of the goals of the authors we survey, our primary aim is to present a comprehensive archive of thought about self-reproducing machines. These chapters represent the most extensive early history of the subject published to date and include coverage of many works that have not been widely discussed elsewhere. We do provide a synthesis and summary of the concepts discussed in Chap. 7, and it is there that we offer more of our own views on the field and where we see it heading.

The audience we have in mind includes anyone wishing to learn about the origins of the idea of self-reproducing and evolving machines, especially those interested in drawing lessons from this early work regarding likely future developments in the field. Most obviously, the audience will be Artificial Life and Artificial Intelligence practitioners. We also believe the subject will be of interest to many philosophers, biologists, engineers, historians of science, and those working in the emerging field of AI safety and ethics.

We hope the content will be of value in informing a wider general readership too. For that reason, in Chap. 1 we discuss the profound future implications of the technology and explain why it is a subject of broad relevance. We have tried to make the text accessible and to avoid technical jargon, although this has not always been possible. In particular, in Chap. 5 we discuss at greater length the details of the first realisations of self-reproducing machines in the 1950s, and in Chap. 7 we summarise technical aspects of the design of self-reproducing machines. Nevertheless, we hope we have found a reasonable balance between technical detail and accessibility, even in these sections.

Having conceived the idea of the book in 2014, the content was primarily researched and written by TT, in between other work, over the next five years. AD provided feedback and ideas during numerous discussions over that period, together with detailed comments and editorial suggestions on drafts of the book.

At the time of writing, the possibilities of self-reproducing and evolving machines are not commonly addressed in popular discussions about robotics and artificial intelligence. However, as you will see in what follows, we argue that work in this area has potentially huge implications for the future of humanity. We hope this book plays a small part in bringing these intriguing and important topics back onto the agenda when considering the deep history and far future of intelligent machines and the fate of our own species.

Additional information and materials relating to the book can be found online at

Tim Taylor, Alan Dorin
Edinburgh & Melbourne
April 2020


Bullock, S. (2008). Charles Babbage and the emergence of automated reason. In P. Husbands, O. Holland, & M. Wheeler (Eds.), The mechanical mind in history (pp. 19–39). MIT Press.