School of Computing and Mathematics

I was appointed as a Lecturer at Keele University in September 2007, having previously been a lecturer at the Universities of Portsmouth and Birmingham. My first degree, BA (Hons; later MA) Mathematics was obtained from the University of Cambridge; my MSc in Knowledge Based Systems from the University of Sussex; and my PhD from the University of Southampton. Prior to university I worked in the software industry. At Keele, I was the Information Technology Management for Business (ITMB) programme director for 2007-8, and have been the programme director for our Computer Science and Information Systems programmes since 2008-9, and for our Creative Computing and Smart Systems programmes since their introduction in 2009-10.

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 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, Elizabeth Aston and Dr Charles Day, also from the Computational Intelligence and Cognitive Science research group, have recently [2011] established and published their discovery that the mutation rate above which individuals with the highest fitnesses are lost from simple evolving populations has an exponential dependence on population size. Elizabeth is now carrying out research on the implications this new understanding has for species under threat of extinction and measures that could be taken to prevent extinctions.

Dr Channon and his other 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 focussed 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 Center for Brain Science, Harvard University), 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 initial research has focused on the evolution of central pattern generators and spiking neural network controllers for articulated virtual creatures.

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 an EPSRC-funded project Dr Channon also works with partners Drs Chris Knight, Rok Krasovec, Roman Belavkin and John Aston, from the Universities of Manchester, Middlesex and Warwick, to advance our understanding of (amongst other things) the evolution of DNA sequences and their bindings to transcription factors and other proteins. The project involves both the evolution of wetware (biological) DNA and the much faster evolution of sequences in computer experiments using tables of DNA to protein binding affinities. In one recent [2011] advance Dr Channon used a meta-Genetic Algorithm to evolve mutation rate curves that show a close match with Dr Belavkin and the research team's theory, and 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.

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.

• CSC-30020 Computational Intelligence II (Module Leader)
• CSC-30019 Games Computing (Module Leader)

http://www.channon.net/alastair/