Dr Alastair Channon

Title: Reader
Phone: 01782 734270
Email: a.d.channon@keele.ac.uk
Location: CR01
Role: Evolutionary Systems Research Theme Lead
Computer Science Research Centre Lead
Contacting me: Please email me for an appointment.

Dr Alastair Channon worked in the software industry (at Micro Focus) before carrying out his BA/MA in Mathematics at the University of Cambridge and then focussing on Evolutionary and Adaptive Computation through an MSc at the University of Sussex and a PhD at the University of Southampton.  From there he moved directly to a senior lectureship (post-92) at the University of Portsmouth in 1999, a lectureship at the University of Birmingham in 2004 and to the School of Computing and Mathematics at Keele University in 2007, where he is now a reader. 

His primary research interest is in the open-ended evolution of neurally controlled animats and he is best known for having created the only closed system other than Earth's biosphere to have passed the enhanced statistical “ALife Test” for open-ended evolution. Alastair's recent publications have included significant results on the relationship of mutation rate to population size, with clear implications for biological extinction events, and to fitness, computed over both abstract and biological fitness landscapes. He is a partner in a £580k BBSRC project on the theory and practice of evolvability: effects and mechanisms of mutation rate plasticity, with partners at Manchester and Middlesex Universities, following the same team's successful completion of a £500k EPSRC project on information dynamics in evolutionary systems. Alastair led the design, proposal and implementation of new programmes at Keele in 2008, leading to Computing undergraduate application and intake numbers increasing by approximately 170% and 80% (FTE) respectively.  He was the Information Technology Management for Business (ITMB) programme director for 2007-8 and has been the programme director for Keele's Computer Science and Information Systems programmes since 2008-9, and for Creative Computing and Smart Systems programmes since their introduction in 2009-10.  His innovative teaching methods are reflected in excellent student feedback. He also specified and assembled the University's GPGPU compute cluster, which has enabled suitably matched research experiments to proceed more than one hundred times as fast as previously possible.


To read more about Dr Channon's research please click on the research and scholarship tab.

Dr Alastair Channon's main research interest is in open-ended evolution, in a vein similar to Tom Ray's work on Tierra (in that the phenotype to fitness mapping is an emergent property of the evolving environment and competition is biotic rather than abiotic) but using neuroevolution in a neutral network-aware paradigm similar to Inman Harvey's SAGA, and with the mid- and long-term aims of overcoming the severe scaling problems exhibited by today's evolutionary algorithms (including the difficulty of formulating evaluation functions for complex behaviours), and evolving true artificial intelligence through natural selection.

He developed and published [2001] the first ever closed artificial system to pass Mark Bedau et al's established statistical “ALife Test” for unbounded evolutionary dynamics. Earth's biosphere (through fossil-record databases) is the only other system to have passed the advanced form of this test. This is a very significant result: potentially a second example of unbounded evolution. The creation of a system capable of passing the test had been identified by Bedau, Snyder and Packard as “among the very highest priorities of the field of artificial life”.

Dr Channon made the test well-grounded even for long-term unbounded evolution in artificial systems, through the first ever method of computing individual genes' adaptive ('normalized') evolutionary activities [2003, 2006]. These refinements have been found by Andrew Stout and Lee Spector [2005] to be crucial in resisting attempts to achieve a classification of unbounded dynamics in “intuitively unlifelike” systems. Dr Channon's work in this area now focuses on using this combination of this evolutionary system and analytical methods to draw generalized conclusions about open-ended evolution that were previously impossible given just the real-world example.

Dr Channon and his PhD students carry out research into the use of evolution to generate increasingly intelligent agents, in a vein similar to Larry Yaeger's Polyworld research but focused increasingly on 3D virtual creatures as first evolved by Karl Sims, to better enable the observation of evolved behaviours as they emerge and (with a view toward open-ended evolution) to provide a more open range of low-level actions. In published work with a past PhD student, Dr Thomas Miconi (now at The Neurosciences Institute), they demonstrated [2005] the first artificial evolution of physically simulated articulated creatures with realistic co-adapted behaviours using general purpose neurons. The previous need for ad hoc (problem-specific) neurons was a barrier to the long-term evolution of general behaviours. Research in this area is now being carried forward by Dr Channon and Adam Stanton, whose research is focused on the incremental neuroevolution of reactive and deliberative behaviour in articulated virtual creatures [2013, 2015].

Another advance came from Dr Channon and his past students, Tim Ellis and Dr Edward Robinson, with their published [2007] demonstration, for the first time ever, of incremental neuro-evolutionary learning on tasks requiring deliberative behaviours: evolved artificial neural controllers that solve tasks which cannot be solved by reactive mechanisms alone and which would traditionally have their solutions formulated in terms of search-based planning. More recently [2011] James Borg and Dr Channon published a paper demonstrating that the introduction of both transcription errors (noise in the genotype to phenotype map) and cultural transmission, in the form of learning by imitation, can enable the artificial evolution of behaviours inaccessible to incremental genetic evolution alone.

Through EPSRC project EP/H031936/1 Evolution as an Information Dynamic System (2010-2013), Dr Channon worked with partners Drs Chris Knight, Rok Krasovec, Roman Belavkin and (now) Professor John Aston, from the Universities of Manchester, Middlesex and Warwick (now Cambridge). Our work on mutation rate plasticity established optimal mutation rate control functions, for both artificial landscapes and natural landscapes defined by DNA-transcription factor affinities, and demonstrated general applicability to biology [2014]. Dr Channon used tables of DNA to protein binding affinities to evolve DNA sequences in computer simulations, and a meta-Genetic Algorithm [2011] to evolve fitness-dependent mutation rate mappings that show a close match with the research team's predictions from information theory. He used Nvidia's CUDA many-core (GPGPU) technology to speed up computational experiments from 1.4 years (maximum, per run) to 3 days, enabling a step change in the rate at which the research was able to advance. The team began the follow-on BBSRC project BB/L009579/1 The theory and practice of evolvability: Effects and mechanisms of mutation rate plasticity in Febrauary 2014. In this project we are primarily investigating the effects and mechanisms of density-dependent mutation rate plasticity, although we first extended our previous work to determine optimal mutation rate control under selection [2015].

Through the above Evolution as an Information Dynamic System project, the team also investigated the relationship between mutation rate, genetic loss and population size. Above a critical mutation rate (CMR) fitter genotypes may be outcompeted by those with greater mutational robustness. Expressed in terms of fitness landscapes, narrow high fitness peaks may be lost by a population while broader, lower peaks are maintained; this phenomena is referred to as “survival of the flattest”. Dr Channon and his then-PhD student (now) Dr Elizabeth Aston, together with other members of the EPSRC project team and Dr Charles Day, also from the Evolutionary Systems research group, established that CMR has an exponential dependence on population size in haploid populations [2011]: that as population size falls, the CMR above which fitter alleles are lost transitions unexpectedly from near-constant (the previous assumption in evolutionary biology) to drop exponentially for small populations, leaving them spiralling toward extinction. We subsequently verified that our model closely reproduces the less significant but established mathematical relationship between population size and 'error threshold' (no mathematical model has yet been derived for CMR), and established that the result also holds for diploid populations [2013]. This and the team's related research has lead to our most recently awarded BBSRC project, BB/M021157/1 Adaptive landscapes of antibiotic resistance: population size and 'survival-of-the-flattest', which will start in August 2015. In this the focus is on the small population of a microbe with a newly arisen antibiotic resistance mutation.

Dr Channon leads the Evolutionary Systems research group in the School of Computing and Mathematics at Keele University. He is a member of EPSRC’s College of Peer Reviewers, IEEE, the IEEE Computational Intelligence Society, the ACM Special Interest Group for Genetic and Evolutionary Computation and the International Society for Artificial Life.

Selected Publications

  • Taylor T, Bedau M, Channon A, Ackley D, Banzhaf W, Beslon G, Dolson E, Froese T, Hickinbotham S, Ikegami T, McMullin B, Packard N, Rasmussen S, Virgo N, Agmon E, Clark E, McGregor S, Ofria C, Ropella G, Spector L, Stanley KO, Stanton A, Timperley C, Vostinar A, Wiser M. 2016. Open-Ended Evolution: Perspectives from the OEE Workshop in York. Artif Life, vol. 22(3), 408-423. link> doi>
  • Stanton A and Channon AD. 2015. Incremental Neuroevolution of Reactive and Deliberative 3D Agents. In P. Andrews, L. Caves, R. Doursat, S. Hickinbotham, F. Polack, S. Stepney, T. Taylor & J. Timmis (Eds.). Proceedings of the European Conference on Artificial Life 2015 (ECAL 2015) (pp. 341-348). MIT Press. doi>
  • Aston E, Channon AD, Belavkin RV, Krasovec R, Knight CG. 2015. Optimal Mutation Rate Control under Selection in Hamming Spaces. Proceedings of the European Conference on Artificial Life 2015 (ECAL 2015) (pp. 640-647). MIT Press. doi>
  • Krašovec R, Belavkin RV, Aston JAD, Channon A, Aston E, Rash BM, Kadirvel M, Forbes S, Knight CG. 2014. Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell-cell interactions. Nat Commun, vol. 5, 3742. link> doi>
  • Stanton A and Channon A. 2013. Heterogeneous complexification strategies robustly outperform homogeneous strategies for incremental evolution. In P. Lio, M. Orazio, G. Nicosia, S. Nolfi & M. Pavone (Eds.). Advances in Artificial Life (pp. 973-980). MIT Press. doi>

Full Publications List show

Journal Articles

  • Krašovec R, Richards H, Gifford DR, Hatcher C, Faulkner KJ, Belavkin RV, Channon A, Aston E, McBain AJ, Knight CG. 2017. Spontaneous mutation rate is a plastic trait associated with population density across domains of life. PLoS Biology, vol. 15(8), e2002731. link> doi> link>
  • Taylor T, Bedau M, Channon A, Ackley D, Banzhaf W, Beslon G, Dolson E, Froese T, Hickinbotham S, Ikegami T, McMullin B, Packard N, Rasmussen S, Virgo N, Agmon E, Clark E, McGregor S, Ofria C, Ropella G, Spector L, Stanley KO, Stanton A, Timperley C, Vostinar A, Wiser M. 2016. Open-Ended Evolution: Perspectives from the OEE Workshop in York. Artif Life, vol. 22(3), 408-423. link> doi>
  • Belavkin RV, Channon A, Aston E, Aston J, Krašovec R, Knight CG. 2016. Monotonicity of fitness landscapes and mutation rate control. Journal of mathematical biology, vol. 73(6), 1491-1524. doi> link>
  • Krašovec R, Belavkin RV, Aston JA, Channon A, Aston E, Rash BM, Kadirvel M, Forbes S, Knight CG. 2014. Where antibiotic resistance mutations meet quorum-sensing. Microb Cell, vol. 1(7), 250-252. link> doi>
  • Krašovec R, Belavkin RV, Aston JAD, Channon A, Aston E, Rash BM, Kadirvel M, Forbes S, Knight CG. 2014. Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell-cell interactions. Nat Commun, vol. 5, 3742. link> doi>
  • Aston E, Channon A, Day C, Knight CG. 2013. Critical mutation rate has an exponential dependence on population size in haploid and diploid populations. PLoS One, vol. 8(12), e83438. link> doi>
  • Channon AD. 2006. Unbounded Evolutionary Dynamics in a System of Agents that Actively Process and Transform Their Environment. Genetic Programming and Evolvable Machines, vol. 7, 253-281. doi>
  • Channon AD and Damper RI. 2000. Towards the evolutionary emergence of increasingly complex advantageous behaviours. INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, vol. 31(7), 843-860. link> doi>

Other

  • Jolley BP, Borg JM, Channon A. 2016. Analysis of Social Learning Strategies When Discovering and Maintaining Behaviours Inaccessible to Incremental Genetic Evolution. FROM ANIMALS TO ANIMATS 14 (vol. 9825, pp. 293-304). link> doi>
  • Aston E, Channon AD, Belavkin RV, Krasovec R, Knight CG. 2016. Critical Mutation Rate has an Exponential Dependence on Population Size for Eukaryotic-Length Genomes. MIT Press.
  • Stanton A and Channon AD. 2016. Neuroevolution of Feedback Control for Object Manipulation by 3D Agents. In C. Gershenson, T. Froese, J. Siqueiros, W. Aguilar, E. Izquierdo & H. Sayama (Eds.). Proceedings of the Artificial Life Conference 2016. MIT Press. doi>
  • Stanton A and Channon AD. 2015. Incremental Neuroevolution of Reactive and Deliberative 3D Agents. In P. Andrews, L. Caves, R. Doursat, S. Hickinbotham, F. Polack, S. Stepney, T. Taylor & J. Timmis (Eds.). Proceedings of the European Conference on Artificial Life 2015 (ECAL 2015) (pp. 341-348). MIT Press. doi>
  • Aston E, Channon AD, Belavkin RV, Krasovec R, Knight CG. 2015. Optimal Mutation Rate Control under Selection in Hamming Spaces. Proceedings of the European Conference on Artificial Life 2015 (ECAL 2015) (pp. 640-647). MIT Press. doi>
  • Stanton A and Channon A. 2013. Heterogeneous complexification strategies robustly outperform homogeneous strategies for incremental evolution. In P. Lio, M. Orazio, G. Nicosia, S. Nolfi & M. Pavone (Eds.). Advances in Artificial Life (pp. 973-980). MIT Press. doi>
  • Borg J and Channon AD. 2012. Testing the Variability Selection Hypothesis: The Adoption of Social Learning in Increasingly Variable Environments. MIT Press. doi>
  • Channon A, Aston E, Day C, Belavkin RV, Knight CG. 2011. Critical mutation rate has an exponential dependence on population size. In T. Lenaerts, M. Giacobini, H. Bersini, P. Bourgine, M. Dorigo & R. Doursat (Eds.). Advances in Artificial Life, ECAL 2011: Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems (pp. 117-124). Heidelberg: MIT Press. doi>
  • Borg JM, Channon A, Day C. 2011. Discovering and maintaining behaviours inaccessible to incremental genetic evolution through transcription errors and cultural transmission. In T. Lenaerts, M. Giacobini, H. Bersini, P. Bourgine, M. Dorigo & R. Doursat (Eds.). Advances in Artificial Life, ECAL 2011: Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems (pp. 101-108). Heidelberg: MIT Press. doi>
  • Belavkin RV, Channon A, Aston E, Aston J, Knight CG. 2011. Theory and practice of optimal mutation rate control in hamming spaces of DNA sequences. In T. Lenaerts, M. Giacobini, H. Bersini, P. Bourgine, M. Dorigo & R. Doursat (Eds.). Advances in Artificial Life, ECAL 2011: Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems (pp. 85-92). Heidelberg: MIT Press. doi>
  • Robinson E, Ellis T, Channon A. 2007. Neuroevolution of agents capable of reactive and deliberative behaviours in novel and dynamic environments. ADVANCES IN ARTIFICIAL LIFE, PROCEEDINGS (vol. 4648, pp. 345-+). link>
  • Miconi T and Channon AD. 2006. An Improved System for Artificial Creatures Evolution. In LMRE. al (Ed.). Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems (ALife X) (pp. 255-261). MIT Press.
  • Miconi T and Channon A. 2006. Analysing co-evolution among artificial 3D creatures. ARTIIFICAL EVOLUTION (vol. 3871, pp. 167-178). link>
  • Miconi T and Channon A. 2006. The N-strikes-out algorithm: A steady-state algorithm for coevolution. 2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6 (pp. 1639-1646). IEEE.
  • Miconi T, Channon A, IEEE. 2005. A virtual creatures model for studies in artificial evolution. 2005 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-3, PROCEEDINGS (pp. 565-572). link>
  • Channon AD. 2003. Improving and still passing the ALife test: Component-normalised activity statistics classify evolution in Geb as unbounded. In RK. Standish, MA. Bedau & HA. Abbass (Eds.). Proceedings of Artificial Life VIII, Sydney (pp. 173-181). Cambridge, MA: MIT Press.
  • Channon AD. 2001. Evolutionary Emergence: The Struggle for Existence in Artificial Biota.
  • Channon A. 2001. Passing the ALife test: Activity statistics classify evolution in Geb as unbounded. ADVANCES IN ARTIFICIAL LIFE (vol. 2159, pp. 417-426). link>
  • Channon AD and Damper RI. 1998. Evolving novel behaviors via natural selection. ARTIFICIAL LIFE VI (pp. 384-388). link>
  • Channon AD and Damper RI. 1998. Perpetuating evolutionary emergence. FROM ANIMALS TO ANIMATS 5 (pp. 534-539). link>
  • Channon AD and Damper RI. 1998. The evolutionary emergence of socially intelligent agents.
  • Channon AD and Damper RI. 1997. The artificial evolution of real intelligence by natural selection.
  • Channon AD. 1996. The Evolutionary Emergence route to Artificial Intelligence.
  • Borg JM and Channon AD. Evolutionary Adaptation to Social Information Use without Learning. European Conference on the Applications of Evolutionary Computation. Springer. doi>
  • Student project supervision