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,
These refinements have been found by Andrew
Stout and Lee
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 focusses 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 is a member of the Computational Intelligence & Cognitive Science Research Group, the Research Institute for the Environment, Physical Sciences and Applied Mathematics (EPSAM), the IEEE, the IEEE Computational Intelligence Society, the ACM Special Interest Group for Genetic and Evolutionary Computation and the International Society for Artificial Life.
R. V. Belavkin, A. Channon, E. Aston, J. Aston and C. G. Knight, ``Monotonicity of Fitness Landscapes and Mutation Rate Control,'' preprint, submitted September 5, 2012.
(99 words:) This paper generalizes Fisher's geometric phenotypic model of adaptation by mutation to a genetic model in Hamming space and derives the probability of adaptation as a function of mutation rate. Theoretical mutation-rate control functions are derived and then evaluated against optimal mutation functions evolved using a meta-genetic algorithm. Experimental results show a close match between theory and experiment, both in artificial fitness landscapes defined by Hamming distance and natural landscape defined by DNA-protein affinities for 115 transcription factors. Further, a revealing correlation is demonstrated for these transcription factors, between the monotonicity of their landscapes and their optimal mutation functions.
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.
(98 words:) This paper provides the first definitive answer to the question of whether or not the variability selection hypothesis is sufficient to explain the adoption of social learning in increasingly variable environments. The hypothesis predicts the adaptation toward and promotion of versatile behaviours and survival strategies (such as social learning) in response to such conditions. The question was tested empirically using combinations of 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.
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. 118-125, MIT Press, 2011.
(100 words:) Slowing the unnaturally high rate of species extinction is the most important challenge facing the world today, necessitating an improved understanding of extinction factors. The simple model presented in this paper reproduces closely the known relationship between population size and 'error threshold' (the mutation rate above which all alleles of a gene are lost) and provides the significant new finding that as population size falls, the lower and so more significant 'critical mutation rate' (above which fitter alleles are lost) transitions from near-constant against population size (the previous assumption) to drop exponentially for small populations, leaving them spiralling toward extinction.
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. 86-93, MIT Press, 2011.
(88 words:) This paper generalizes Fisher's geometric phenotypic model of adaptation by mutation to a genetic model in Hamming space and derives the probability of adaptation as a function of mutation rate. Optimal control rules for mutation rate are derived explicitly for some 'relatively monotonic' landscapes. These theoretical control functions are then evaluated against optimal mutation functions evolved using a meta-genetic algorithm. Experimental results show a close match between theory and experiment, both in artificial fitness landscapes defined by Hamming distance and a natural landscape defined by a DNA-protein affinity.
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. 102-109, MIT Press, 2011.
(100 words:) 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.
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.
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, vol. 7, no. 3, pp. 253–281, 2006.
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.
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.
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.
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.
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 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, vol. 31, no. 7, pp. 843–860, 2000.
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.
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