Julian Francis Miller, 1955–2022

Susan Stepney
University of York, UK

Department of Computer Science

Alan Dorin
Monash University, Australia

Department of Data Science and AI

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Julian Francis Miller1

It is with great sadness that we report the death of our colleague and friend, Julian Miller.

Julian’s work is well known throughout the Artiﬁcial Life community: His Cartesian genetic pro-
gramming (CGP) and in materio computing are foundational concepts. He also made contributions
in morphological computing and neurocomputing, all based on his fascination with evolution as a
means of attacking and solving problems. Like many in the ALife community, he had an interdis-
ciplinary career, commencing with a ﬁrst degree in Physics and a PhD in Mathematics, followed by
research in Natural Computing and material computing at the universities of Napier, Birmingham,
and York in the UK.

Julian invented CGP (Miller, 1999), a way of encoding graph programs (functional nodes con-
nected by edges) in a string of integers, allowing the string to be evolved in the standard way, with
the graph (located on a Cartesian grid, hence its name) produced as the result of a genotype to
phenotype mapping. From this simple beginning, Julian and his students continued to develop the
approach, and other researchers joined in. Ten years later, the ﬁeld had grown signiﬁcantly, with
many researchers both using CGP in their own work and extending the original concept. Indeed,
the ﬁeld had grown enough that Julian could edit an entire book on the topic (Miller, 2011).

Stepney and Dorin

Julian Francis Miller, 1955–2022

Ten years later still, the ﬁeld shows no signs of abating, and Julian wrote a 40-page review for
Genetic Programming and Evolvable Machines on CGP’s status, its many variants, and its future prospects
(Miller, 2020).

Julian was also a pioneer in the ﬁeld of in materio computing (Miller & Downing, 2002), which ex-
ploits the physical properties of unconventional materials, such as liquid crystals (Harding & Miller,
2004) and carbon nanotubes (Miller et al., 2014), to perform computation intrinsically, in what he
dubbed a “Field Programmable Matter Array.” His original work used evolutionary algorithms di-
rectly to conﬁgure the materials. Later, he also used Reservoir Computing as a more abstract model
for getting these materials to compute (Dale et al., 2017). This is another ﬁeld with explosive growth,
so much so that some authors have even published on the name of the domain itself (Ricciardi &
Milano, 2022). Julian was there from the start, contributing his insights and ideas throughout.

CGP and in materio computing may be what Julian is best known for, but these contributions
were embedded in a deeper research program of understanding development as a fundamental com-
ponent of evolving embodied computation. From growing a self-repairing “French-ﬂag” organism
(Miller, 2004), to assembling complex structures through Artiﬁcial Chemistries (Faulconbridge et al.,
2011), to the idea of the “software garden” (Miller, 2018), Julian felt that both growth and evolution
are essential concepts in complex systems.

His Festschrift Inspired by Nature (Stepney & Adamatzky, 2018) was a (slightly late) 60th birth-
day present from his many academic colleagues. It includes chapters contributed by a wide range
of authors who have built on and been inspired by his many research interests. The text covers
evolution and hardware, CGP applications, chemistry, and development. Julian retired in 2016, but
he did not stop his research. He used the freedom from the quotidian constraints of an academic
job to pursue a new interest. He was bringing together his discoveries in evolution, development,
networks, and computation to develop a new neural model to evolve programs that build, or grow,
neural networks. His most recent publication on that topic has only just appeared (Miller, 2022).

Julian’s retirement also allowed him to spend time with his recently acquired beloved new fam-
ily. His wife Gabi remembers him thus: “He was a loving and generous-hearted husband, a wise
step-father to my three grown adults and much loved Grandpa to our four grand-children. Jules will
be sadly missed, but also lovingly remembered by all whose life he touched.” Many further tributes
to Julian from his colleagues can be found in the latest SIGEVO newsletter (Ochoa, 2022).

References
Dale, M., Miller, J. F., & Stepney, S. (2017). Reservoir computing as a model for in-materio computing.

In A. Adamatzky (Ed.), Advances in Unconventional Computing (pp. 533–571). Springer. https://doi.org/10
.1007/978-3-319-33924-5_22

Faulconbridge, A., Stepney, S., Miller, J. F., & Caves, L. S. D. (2011). RBN-World: A subsymbolic artiﬁcial

chemistry. ECAL 2009, 5777, 377–384. https://doi.org/10.1007/978-3-642-21283-3_47

Harding, S., & Miller, J. F. (2004). Evolution in materio: Initial experiments with liquid crystal. In Proceedings,

2004 NASA/DoD Conference on Evolvable Hardware. https://doi.org/10.1109/EH.2004.1310844

Miller, J. F. (1999). An empirical study of the efﬁciency of learning boolean functions using a Cartesian
Genetic Programming approach. In Proceedings of the 1st Annual Conference on Genetic and Evolutionary
Computation—Volume 2, 1135–1142.

Miller, J. F. (2004). Evolving a self-repairing, self-regulating, french ﬂag organism. In GECCO 2004, 129–139.

https://doi.org/10.1007/978-3-540-24854-5_12

Miller, J. F. (Ed.). (2011). Cartesian genetic programming. Springer. https://doi.org/10.1007/978-3-642-17310-3

Miller, J. F. (2018). The software garden. In R. Walsh & S. Stepney (Eds.), Narrating complexity (pp. 201–212).

Springer. https://doi.org/10.1007/978-3-319-64714-2_15

Miller, J. F. (2020). Cartesian genetic programming: Its status and future. Genetic Programming and Evolvable

Machines, 21(1), 129–168. https://doi.org/10.1007/s10710-019-09360-6

Miller, J. F. (2022). IMPROBED: Multiple problem-solving brain via evolved developmental programs.

Artiﬁcial Life, 27(3–4), 300–335. https://doi.org/10.1162/artl_a_00346

Artiﬁcial Life Volume 28, Number 1

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Stepney and Dorin

Julian Francis Miller, 1955–2022

Miller, J. F., & Downing, K. (2002). Evolution in materio: Looking beyond the silicon box. In Proceedings, 2002
NASA/DoD Conference on Evolvable Hardware, 167–176. https://doi.org/10.1109/EH.2002.1029882

Miller, J. F., Harding, S. L., & Tufte, G. (2014). Evolution-in-materio: Evolving computation in materials.

Evolutionary Intelligence, 7(1), 49–67. https://doi.org/10.1007/s12065-014-0106-6

Ochoa, G. (Ed.). (2022). Tributes to Julian F. Miller (1955–2022). SIGEVOlution: Newsletter of the ACM Special
Interest Group on Genetic and Evolutionary Computation, 15(1). https://evolution.sigevo.org/issues/HTML
/sigevolution-15-1/home.html#h.6uzth8kptc5x, https://doi.org/10.1145/3532942.3532943

Ricciardi, C., & Milano, G. (2022). In materia should be used instead of in materio. Frontiers in Nanotechnology, 4.

https://doi.org/10.3389/fnano.2022.850561

Stepney, S., & Adamatzky, A. (Eds.). (2018). Inspired by nature: Essays presented to Julian F. Miller on the occasion of

his 60th birthday. Springer.

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Artiﬁcial Life Volume 28, Number 1 Julian Francis Miller, 1955–2022 image

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