Simulated evolution is well established as a computational optimization methodology. However, this methodology has so far concentrated on evolution towards pre-specified goals: a route which cannot generate anything like the unbounded evolution or the diversity and complexity of structures that we observe in nature. In “On the Origin of Species” Darwin emphasized the difference between the struggle between organisms for limited resources (biotic competition) and the struggle against features such as drought, of the non-living physical environment (abiotic competition). Biotic competition, he argued, has been the cause of sustained evolutionary progress.
Neo-Darwinism, which adds Mendelian heredity and post-Mendelian genetic theory, has clarified the nature and origins of species to the extent that we can carry out evolutionary experiments within artificial systems such as computer simulations and robotics. However, within the fields of artificial evolution (including evolutionary/genetic algorithms/programming/computing and sub-fields of artificial life, adaptive behaviour and digital biota), work to date uses only abiotic competition, with very few exceptions. Most of Darwin's theory would seem to have been ignored. Where biotic competition has been used, serious problems of evolvability can be identified.
The main focus is now on the generation of a system that exhibits unbounded evolution. Bedau and Packard's evolutionary activity statistics provide a basis for testing for this and have already been applied to a number of both artificial and natural selection systems, including my own. From the successes and failures of these tests we are beginning to extend the list of known requirements for unbounded evolution. Beyond this, biologists will want to use such systems to draw generic conclusions about evolution and engineers will want to evolve solutions that we can find uses for.
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  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  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  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  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  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 . Dr Channon used tables of DNA to protein binding affinities to evolve DNA sequences in computer simulations, and a meta-Genetic Algorithm  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 .
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 : 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 . 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 the IEEE, the IEEE Computational Intelligence Society, the ACM Special Interest Group for Genetic and Evolutionary Computation and the International Society for Artificial Life.
T. Taylor, M. A. Bedau, A. Channon, D. Ackley, W. Banzhaf, G. Beslon, E. Dolson, T. Froese, S. Hickinbotham, T. Ikegami, B. McMullin, N. Packard, S. Rasmussen, N. Virgo, E. Agmon, E. Clark, S. McGregor, C. Ofria, G. Ropella, L. Spector, K. O. Stanley, A. Stanton, C. Timperley, A. Vostinar and M. Wiser, Open-Ended Evolution: Perspectives from the OEE Workshop in York, Artificial Life 22: 408-423, 2016. doi:10/bpqb
B. P. Jolley, J. M. Borg and A. Channon, Analysis of Social Learning Strategies when Discovering and Maintaining Behaviours Inaccessible to Incremental Genetic Evolution, in From Animals to Animats 14: Proceedings of the Fourteenth International Conference on Simulation of Adaptive Behavior (SAB 2016), pp. 293-304, Springer, 2016. doi:10/bpp9
A. Stanton and A. Channon, Neuroevolution of Feedback Control for Object Manipulation by 3D Agents, in Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems (ALife XV), pp. 144-151, MIT Press, 2016. doi:10/bq8c
E. Aston, A. Channon, R. V. Belavkin, R. Krasovec and C. G. Knight, Critical Mutation Rate has an Exponential Dependence on Population Size for Eukaryotic-Length Genomes, in Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems (ALife XV), pp. 172-179, MIT Press, 2016. doi:10/bq8d
R. V. Belavkin, A. Channon, E. Aston, J. Aston, R. Krasovec and C. G. Knight, Monotonicity of fitness landscapes and mutation rate control, Journal of Mathematical Biology 73(6): 1491-1524, 2016. doi:10/bd7r
A. Stanton and A. Channon, Incremental Neuroevolution of Reactive and Deliberative 3D Agents, in Proceedings of the European Conference on Artificial Life 2015 (ECAL 2015), pp. 341-348, MIT Press, 2015. doi:10/59x
E. Aston, A. Channon, R. V. Belavkin, R. Krasovec and C. G. Knight, Optimal Mutation Rate Control under Selection in Hamming Spaces, in Proceedings of the European Conference on Artificial Life 2015 (ECAL 2015), pp. 640-647, MIT Press, 2015. doi:10/59z
R. Krasovec, R. V. Belavkin, J. A. D. Aston, A. Channon, E. Aston, B. M. Rash, M. Kadirvel, S. Forbes and C. G. Knight, Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell–cell interactions, Nature Communications 5:3742, 2014. doi:10/skb
R. Krasovec, R. V. Belavkin, J. A. D. Aston, A. Channon, E. Aston, B. M. Rash, M. Kadirvel, S. Forbes and C. G. Knight, Where antibiotic resistance mutations meet quorum-sensing, Microbial Cell 1(7): 250-252, 2014. doi:10/xzg
E. Aston, A. Channon, C. Day and C. G. Knight, Critical Mutation Rate Has an Exponential Dependence on Population Size in Haploid and Diploid Populations, PLOS ONE 8(12): e83438, 2013. doi:10/qqc
A. Stanton and A. Channon, Heterogeneous complexification strategies robustly outperform homogeneous strategies for incremental evolution, in Advances in Artificial Life, ECAL 2013: Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems, pp. 973-980, MIT Press, 2013. doi:10/nnb
This paper is the first to identify a significant weakness in the standard approaches taken in neuroevolutionary bioinspired robotics: that directly presenting evolution with the most challenging behaviour or increasing complexity up to that point fail, for even a simple generalisable behaviour, to achieve full or even good coverage of behaviour, with no runs achieving full coverage and at most 2% achieving 95% coverage. The paper presents a solution by which 20% of runs achieve full coverage and 48% achieve 95% coverage. This dramatically improves the quality of incremental evolutionary search for complex behaviours.
J. M. Borg and A. Channon, Testing the Variability Selection Hypothesis: The Adoption of Social Learning in Increasingly Variable Environments, in Proceedings of the Thirteenth International Conference on the Simulation and Synthesis of Living Systems (ALife XIII), pp. 317–324, MIT Press, 2012. doi:10/m7p
This paper provides the first definitive answer to the question of whether or not the variability selection hypothesis posed by Potts (Director, Smithsonian Human Origins Program) is sufficient to explain the adoption of social learning in increasingly variable environments. The question was tested empirically using combinations of simulated genetic evolution, individual learning and social learning. Both increasingly-variable and high-variability environments were found to be sufficient to provide an adaptive advantage to populations exhibiting the extra-genetic learning strategies, with social learning favoured over individual learning. These existence proofs add credence to Potts's hypothesis and would have been impossible without such simulation.
J. M. Borg, A. Channon and C. Day, Discovering and Maintaining Behaviours Inaccessible to Incremental Genetic Evolution Through Transcription Errors and Cultural Transmission, in Advances in Artificial Life, ECAL 2011: Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems, pp. 101-108, MIT Press, 2011. doi:10/m7k
This paper presents the first example of a behaviour inaccessible to incremental genetic evolution alone being evolved through the addition of cultural transmission: a significant advance in neuroevolutionary artificial intelligence. Novel behaviours arise through transcription errors (adding noise to the genotype to phenotype map), enabling longer jumps between behaviours than possible by genetic mutation, due to the error threshold. Incorporating noise here rather than in the phenotype to fitness map enables cultural transmission (here learning by imitation) to maintain novel successful behaviours in the population. The combination of these two mechanisms introduces a new way of thinking about evolutionary progress.
A. Channon, E. Aston, C. Day, R. V. Belavkin and C. G. Knight, Critical Mutation Rate Has an Exponential Dependence on Population Size, in Advances in Artificial Life, ECAL 2011: Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems, pp. 117-124, MIT Press, 2011. doi:10/m7m
As highlighted by E. O. Wilson, slowing the unnaturally high rate of species extinction is today's most important global challenge, necessitating an improved understanding of extinction factors. This paper reveals for the first time that as population size falls, the 'critical mutation rate' 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. This new understanding has great potential to aid population management and prevent extinctions. The model closely reproduces the less significant relationship between population size and 'error threshold'. EPSRC project EP/H031936/1.
R. V. Belavkin, A. Channon, E. Aston, J. Aston and C. G. Knight, Theory and Practice of Optimal Mutation Rate Control in Hamming Spaces of DNA Sequences, in Advances in Artificial Life, ECAL 2011: Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems, pp. 85-92, MIT Press, 2011. doi:10/m7j
This paper generalizes Fisher's famous 1930's geometric phenotypic model of adaptation to a genetic model and derives probability of adaptation as a function of mutation rate. Further, these theoretical functions are evaluated against optimal mutation functions evolved using a meta-genetic algorithm. Experimental results verify the new theory, in both artificial landscapes and a natural landscape defined by DNA-transcription factor affinities. The work was undertaken within EPSRC project EP/H031936/1 and formed the theoretical underpinning to both the authors' Nature Communications 2014 paper and our 2014-2017 BBSRC project BB/L009579/1, which seeks to advance knowledge of bacterial antibiotic resistance.
E. Robinson, T. Ellis and A. D. Channon, Neuroevolution of Agents Capable of Reactive and Deliberative Behaviours in Novel and Dynamic Environments, in Advances in Artificial Life: Proceedings of the Ninth European Conference on Artificial Life (ECAL 2007), pp. 345-354, Springer-Verlag, 2007. LNCS volume 4648, in the LNAI subseries. doi:10/bwzbm9
This paper presents evolved artificial neural controllers that solve tasks requiring deliberative behaviours: tasks that cannot be solved by reactive mechanisms alone and which would traditionally have their solutions formulated in terms of search-based planning. Two very different neural networks are used: one that controls high-level deliberative behaviours, such as the selection of sub-goals, and one that provides reactive and navigational capabilities. Animats controlled by a hybrid of these network architectures are evolved in novel and dynamic environments, on increasingly complex versions of an example problem. The results demonstrate, for the first time ever, incremental neuro-evolutionary learning on such tasks.
A. D. Channon, Unbounded evolutionary dynamics in a system of agents that actively process and transform their environment, Genetic Programming and Evolvable Machines 7(3): 253–281, 2006. doi:10/b74rxr
This paper presents significant improvements to the established statistical "ALife Test" for unbounded evolutionary dynamics. Its contribution is in making the revised test (which the system from publication 8 still passes) well-grounded even for long-term unbounded evolution in artificial systems, through the first ever method of computing individual genes' adaptive ('normalized') evolutionary activities. In their paper "Validation of evolutionary activity metrics for long-term evolutionary dynamics", Stout and Spector attempted to "break" the test by achieving unbounded dynamics in "intuitively unlifelike" systems. They concluded that Channon's method of activity normalization is of particular importance to the test's robustness against such attempts.
T. Miconi and A. D. Channon, The N-Strikes-Out Algorithm: A Steady-State Algorithm for Coevolution, in Proceedings of the 2006 IEEE Congress on Evolutionary Computation (CEC 2006), IEEE World Congress on Computational Intelligence (WCCI 2006), Vancouver, (G. G. Yen et al., eds.), pp. 1639–1646, IEEE Press, 2006. doi:10/b4b789
T. Miconi and A. D. Channon, Analysing coevolution among artificial 3D creatures, in Proceedings of the 7th International Conference on Artificial Evolution (Evolution Artificielle 2005): Revised Selected Papers, Lille, (E. G. Talbi et al., eds.), pp. 167–178, Springer, 2006. A volume of the LNCS Series. doi:10/fhz979
This paper presents new accomplishments in the coevolution of neurally controlled agents, and introduces improved methods of coevolutionary analysis. The experiments reported, on the coevolution of physically simulated articulated creatures, are the first to demonstrate 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 new, emergent behaviours. Novel behaviours are identified using an improved coevolutionary analysis method that is both more informative and an order of magnitude cheaper than the original. Finally, individuals are cross-validated between evolutionary runs, in an improved procedure for evaluating global performance.
T. Miconi and A. D. Channon, An Improved System for Artificial Creatures Evolution, in Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems (ALife X), Bloomington, Indiana (L. M. Rocha et al., eds.), pp. 255–261, MIT Press, 2006.
T. Miconi and A. D. Channon, A virtual creatures model for studies in artificial evolution, in Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh (D. Corne et al., eds.), volume 1, pp. 565–572, IEEE Press, 2005. doi:10/d95m2t
A. D. Channon, Improving and still passing the ALife test: Component-normalised activity statistics classify evolution in Geb as unbounded, in Proceedings of Artificial Life VIII, Sydney (R. K. Standish, M. A. Bedau and H. A. Abbass, eds.), (Cambridge, MA), pp. 173–181, MIT Press, 2003. Instructions for replicating the runs discussed in this paper.
A. D. Channon, Passing the ALife test: Activity statistics classify evolution in Geb as unbounded, in Advances in Artificial Life: Proceedings of the Sixth European Conference on Artificial Life (ECAL2001), Prague (J. Kelemen and P. Sosik, eds.), (Heidelberg), pp. 417–426, Springer Verlag, 2001. A volume of the LNCS/LNAI Series. doi:10/cs5jkx
This paper presents the first ever closed artificial system to pass the established statistical "ALife Test" for unbounded evolutionary dynamics: an achievement identified by Bedau, Snyder and Packard as "among the very highest priorities of the field of artificial life". Earth's biosphere (through fossil-record databases) is the only other system to have passed, although many have been evaluated. This is a very significant result: we can now begin to draw generalized conclusions about open-ended evolution that were previously impossible given just the real-world example. It significantly advances our ability to generate emergent processes and structures, including complex and intelligent ones.
A. D. Channon, Evolutionary Emergence: The Struggle for Existence in Artificial Biota, PhD thesis, Department of Electronics and Computer Science, University of Southampton, 2001.
A. D. Channon, Three evolvability requirements for open-ended evolution, in Artificial Life VII Workshop Proceedings (C. C. Maley and E. Boudreau, eds.), (Portland, OR), pp. 39–40, 2000.
A. D. Channon, The Importance of Brain-Body Coevolution in the Natural Selection of AI-Life, in Artificial Life VII Workshop Proceedings (C. C. Maley and E. Boudreau, eds.), (Portland, OR), 2000.
A. D. Channon and R. I. Damper, Towards the evolutionary emergence of increasingly complex advantageous behaviours, International Journal of Systems Science, special issue on Emergent Properties of Complex Systems 31(7): 843–860, 2000. doi:10/cjvq52
A. D. Channon and R. I. Damper, The evolutionary emergence of socially intelligent agents, in Socially Situated Intelligence: a workshop held at SAB'98, University of Zurich Technical Report (B. Edmonds and K. Dautenhahn, eds.), (Zurich), pp. 41–49, 1998.
A. D. Channon and R. I. Damper, Perpetuating evolutionary emergence, in From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior (SAB98), Zurich (R. Pfeifer, B. Blumberg, J. A. Meyer, and S. Wilson, eds.), (Cambridge, MA), pp. 534–539, MIT Press, 1998.
A. D. Channon and R. I. Damper, Evolving novel behaviors via natural selection, in Proceedings of Artificial Life VI, Los Angeles (C. Adami, R. Belew, H. Kitano, and C. Taylor, eds.), (Cambridge, MA), pp. 384–388, MIT Press, 1998.
A. D. Channon and R. I. Damper, The artificial evolution of real intelligence by natural selection. Published on the web site of and poster presented at the Fourth European Conference on Artificial Life (ECAL97), Brighton, 1997.
A. D. Channon, The Evolutionary Emergence route to Artificial Intelligence. MSc thesis, School of Cognitive and Computing Sciences, University of Sussex, 1996.
Geb is an artificial world containing organisms which evolve by natural selection. The papers above provide the best description of Geb. I recommend reading at least Perpetuating evolutionary emergence before trying to make sense of the source code, and that Passing the ALife test is the next paper you read.
Dr Channon has delivered research-led undergraduate and postgraduate modules on computational intelligence, evolutionary systems, intelligent systems, nature inspired design and artificial intelligence, as well as less specialized modules on AI programming, games computing, object oriented programming in C++, information systems, software engineering and group software engineering.
He has been the programme director for Birmingham University's MSc Natural Computation and MSc Intelligent Systems Engineering (which he introduced), for Keele University's ITMB programme (2007-2008) and, since 2008, for Keele University's Computer Science, Information Systems, Creative Computing and Smart Systems degree programmes.