Background   |   Research   |   Publications   |   Software   ||   Teaching   |   Contact   Research Context 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.

Publications

1. J. M. Borg and A. Channon, The Effect of Social Information Use Without Learning on the Evolution of Social Behavior, Artificial Life 26(4): 431-454, 2021. doi:10/gg98

2. B. Jackson and A. Channon, A Simple 3D-Only Evolutionary Bipedal System with Albatross Morphology for Increased Performance, in Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI 2020, including IEEE Artificial Life 2020), pp. 1-8, IEEE, 2020. doi:10/fp7b

3. C. Mennan, Timothy Hopkins, Alastair Channon, Mark Elliott, Brian Johnstone,Timor Kadir, John Loughlin, Mandy Peffers, Andrew Pitsillides, Nidhi Sofat, Caroline Stewart, Fiona E. Watt, Eleftheria Zeggini, Cathy Holt, Sally Roberts and The OATech Network+ Consortium, The use of technology in the subcategorisation of osteoarthritis: a Delphistudy approach, Osteoarthritis and Cartilage Open 2(3): 100081, 2020. doi:10/gsds

4. B. Jackson and A. Channon, Neuroevolution of Humanoids that Walk Further and Faster with Robust Gaits, in Proceedings of the 2019 Conference on Artificial Life, pp. 543-550, MIT Press, 2019. doi:10/ffzz

5. A. Channon, Maximum Individual Complexity is Indefinitely Scalable in Geb, Artificial Life 25(2): 134-144, 2019. In the Open-Ended Evolution II Special Issue. doi:10/c6mn

6. N. Packard, M. Bedau, A. Channon, T. Ikegami, S. Rasmussen, K. Stanley and T. Taylor, An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue, Artificial Life 25(2): 93-103, 2019. doi:10/c6mq

7. N. Packard, M. Bedau, A. Channon, T. Ikegami, S. Rasmussen, K. Stanley and T. Taylor, Open-Ended Evolution and Open-Endedness: Editorial Introduction to the Open-Ended Evolution I Special Issue, Artificial Life 25(1): 1-3, 2019. doi:10/c6mp

8. D. R. Gifford, R. Krasovec, E. Aston, R. V. Belavkin, A. Channon and C. G. Knight, Environmental pleiotropy and demographic history direct adaptation under antibiotic selection, Heredity, 2018. doi:10/ctm6

9. B. Jolley and A. Channon, Evolving Robust, Deliberate Motion Planning With a Shallow Convolutional Neural Network, in Proceedings of the 2018 Conference on Artificial Life, pp. 536-543, MIT Press, 2018. doi:10/ctfq

10. R. Krasovec, H. Richards, D. R. Gifford, R. V. Belavkin, A. Channon, E. Aston, A. J. McBain and C. G. Knight, Opposing effects of final population density and stress on Escherichia coli mutation rate, The ISME Journal, 2018. doi:10/cst8

11. E. Aston, A. Channon, R. V. Belavkin, D. R. Gifford, R. Krasovec and C. G. Knight, Critical Mutation Rate has an Exponential Dependence on Population Size for Eukaryotic-length Genomes with Crossover, Scientific Reports 7: 15519, 2017. doi:10/cf8w

12. B. Jolley and A. Channon, Toward Evolving Robust, Deliberate Motion Planning With HyperNEAT, in Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI 2017, including IEEE Artificial Life 2017), pp. 1-8, IEEE, 2018. doi:10/cqjw

13. R. Krasovec, H. Richards, D. R. Gifford, C. Hatcher, K. J. Faulkner, R. V. Belavkin, A. Channon, E. Aston, A. J. McBain and C. G. Knight, Spontaneous mutation rate is a plastic trait associated with population density across domains of life, PLOS Biology 15(8): e2002731, 2017. doi:10/cb9s

14. E. Aston, A. Channon, R. V. Belavkin, D. Gifford, R. Krasovec and C. G. Knight, Critical Mutation Rate in a Population with Horizontal Gene Transfer, in Proceedings of the European Conference on Artificial Life 2017 (ECAL 2017), pp. 446-453, MIT Press, 2017. doi:10/ch3n

15. J. M. Borg and A. Channon, Evolutionary Adaptation to Social Information Use Without Learning, in Applications of Evolutionary Computation: 20th European Conference (EvoApplications 2017), pp. 837-852, Springer, 2017. doi:10/b88c

16. 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(3): 408-423, 2016. doi:10/bpqb

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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.

27. 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.

28. 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.

29. 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.

30. 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.

31. 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.

32. 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.

33. 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

34. 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.

35. 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.

36. 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

37. 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.

38. 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.

39. A. D. Channon, Evolutionary Emergence: The Struggle for Existence in Artificial Biota, PhD thesis, Department of Electronics and Computer Science, University of Southampton, 2001.

40. 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.

41. 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.

42. 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

43. 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.

44. 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.

45. 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.

46. A. D. Channon and R. I. Damper, The artificial evolution of real intelligence by natural selection. Published on the website of and poster presented at the Fourth European Conference on Artificial Life (ECAL97), Brighton, 1997.

47. A. D. Channon, The Evolutionary Emergence route to Artificial Intelligence. MSc thesis, School of Cognitive and Computing Sciences, University of Sussex, 1996.

Software

Geb Version 8

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.

Teaching and Administration

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.

Contact Details

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