Larry Bull





I am Professor of Artificial Intelligence based in the Department of Computer Science and Creative Technologies at UWE. I teach biology-inspired computing. My main research interest is evolution, the computational modelling of natural systems and its use in artificial systems. Current interests include the evolution of sex and 3D printing. And I am the founding Editor-in-Chief of the Springer journal Evolutionary Intelligence.

Teaching


Publications

(available here)

    Coevolutionary Computation

    • Bull, L. (1995) Artificial Symbiology: evolution in cooperative multi-agent environments. PhD Dissertation, UWE.
    • Bull, L. (ed)(1998) Coevolutionary Computation: Darwinian Multi-Agent Systems. Unpublished.
    • Fogel, G. et al. (eds)(2010) Proceedings of the IEEE Congress on Evolutionary Computation Conference. IEEE Press. (Technical Chair)
    • Smith, A. et al. (eds)(2011) Proceedings of the IEEE Congress on Evolutionary Computation Conference. IEEE Press. (Program Chair)
    • Bull, L. (ed)(2014) Evolutionary Computing 20: Proceedings of the 50th Anniversary AISB Convention. AISB. (Symposium Chair)
      Fundamentals
    • Bull, L. (1997) Evolutionary Computing in Multi-Agent Environments: Partners. In T.Baeck (ed) Proceedings of the Seventh International Conference on Genetic Algorithms. Morgan Kaufmann, pp370-377.
    • Bull, L. (1998) Evolutionary Computing in Multi-Agent Environments: Operators. In V.W. Porto, N. Saravanan, D. Wagen & A.E. Eiben (eds) Proceedings of the Seventh Annual Conference on Evolutionary Programming. Springer Verlag, pp43-52.
    • [j] Bull, L. (2001) On Coevolutionary Genetic Algorithms. Soft Computing 5(3): 201-207.
    • Aickelin, U. & Bull, L. (2002) Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm. In W.B.Langdon, E.Cantu-Paz, K.Mathias, R. Roy, D.Davis, R. Poli, K.Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A.C. Schultz, J. F. Miller, E. Burke & N.Jonoska (eds) GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp263-270.
    • [j] Aickelin, U. & Bull, L. (2003) On Hierarchical Coevolutionary Genetic Algorithms: Recombination and Evaluation Partners. Applied System Sciences 4(2):2-17.
    • [b] Vidgen, R. & Bull, L. (2011) Applications of Kauffman's NKCS model to Management and Organizational Studies. In P. Allen, S. Maguire & B. McKelvey (eds) The SAGE Handbook of Complexity and Management. Sage, pp.201-219.
    • Macro-level Operators: Speciation, Symbiogenesis and Gene Sharing
    • Bull, L. & Fogarty, T.C. (1996) Evolutionary Computing in Cooperative Multi-Agent Systems. In S. Sen (ed) Proceedings of the 1996 AAAI Symposium on Adaptation, Coevolution and Learning in Multi-agent Systems. AAAI, pp22-27.
    • Bull, L. & Fogarty, T.C. (1996) Evolutionary Computing in Multi-Agent Environments: Speciation and Symbiogenesis. In H-M. Voigt, W. Ebeling, I. Rechenberg & H-P. Schwefel (eds) Parallel Problem Solving from Nature - PPSN IV. Springer Verlag, pp12-21.
    • [j] Bull, L. (1999) On Evolving Social Systems. Computational and Mathematical Organization Theory 5(3):281-298.
    • Bull, L. (2005) Coevolutionary Species Adaptation Genetic Algorithms: Growth and Mutation on Coupled Fitness Landscapes. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE Press, pp559-564.
    • Bull, L. (2005) Coevolutionary Species Adaptation Genetic Algorithms: A Continuing SAGA on Coupled Fitness Landscapes. In M. Capcarrere et al. (eds) Proceedings of the Eighth European Conference on Artificial Life. Springer, pp322-331.
      Homogeneous Systems: Collective Agents and Cellular Automata
    • Bull, L. & Holland, O. (1997) Evolutionary Computing in Multi-Agent Environments: Eusociality. In J.R. Koza, K. Deb, M. Dorigo, D.B. Fogel, M. Garzon, H. Iba & R.L. Riolo (eds) Proceedings of the Second Annual Conference on Genetic Programming. Morgan Kaufmann, pp347-352.
    • [j] Bull, L. (2003) Simple Models of Coevolutionary Genetic Algorithms. Artificial Life and Robotics 5(1):58-65.
    • Sapin, E., & Bull, L. (2007) Searching for Glider Guns in Cellular Automata: Exploring Evolutionary and Other Techniques. In N. Monmarche et al. (eds) Artificial Evolution: Proceedings of the 8th International Conference on Evolution Artificielle. Springer, pp255-265.
    • Sapin, E., Bull, L. & Adamatzky, A. (2007) A Genetic Approach to Search for Glider Guns in Cellular Automata. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE Press, pp2456-2462.
    • [j] Adamatzky, A., Bull, L., Collet, P. & Sapin, E. (2008) Evolving Localizations in Reaction-Diffusion Cellular Automata. International Journal of Modern Physics C 19(4): 557-567.
    • [j] Sapin, E. & Bull, L. (2008) Evolutionary Search for Cellular Automata Logic Gates with Collision-based Computing. Complex Systems 17(4): 321-338.
    • [j] Sapin, E., Bull, L. & Adamatzky, A. (2009) Genetic Approaches to Search for Computing Patterns in Cellular Automata. IEEE Computational Intelligence Magazine 4(3): 20-28.
    • [j]Stone, C. & Bull, L. (2009) Evolution of Cellular Automata with Memory: The Density Classification Task. BioSystems 97(2): 108-116.
    • [j] Sapin, E., Collet, P., Adamatzky, A. & Bull, L. (2010) Stochastic Automated Search Methods in Cellular Automata: The Discovery of Tens of Thousands Glider Guns. Natural Computing 9(3):513-543.
    • Genetic Programming: Automatic Function Specification (Speciation)
    • Ahluwalia, M., Bull, L. & Fogarty, T.C. (1997) Coevolving Functions in Genetic Programming: A Comparison in ADF Selection Strategies. In J.R. Koza, K. Deb, M. Dorigo, D.B. Fogel, M. Garzon, H. Iba & R.L. Riolo (eds) Proceedings of the Second Annual Conference on Genetic Programming. Morgan Kaufmann, pp3-8.
    • Ahluwalia, M., Bull, L. & Fogarty, T.C. (1997) Coevolving Functions in Genetic Programming: An Emergent Approach using ADFs and GLiB. In J.R. Koza (ed) Late Breaking Papers at the Genetic Programming 1997 Conference. Stanford University, pp1-6.
    • Ahluwalia, M. & Bull, L. (1998) Coevolving Functions in Genetic Programming: Dynamic ADF Creation using GLiB. In V.W. Porto, N. Saravanan, D. Wagen & A.E. Eiben (eds) Proceedings of the Seventh Annual Conference on Evolutionary Programming. Springer Verlag, pp809-818.
    • Ahluwalia, M. & Bull, L. (1999) Coevolving Functions in Genetic Programming: Classification using K-nearest-neighbour. In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela & R.E. Smith (eds) GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp947-952.
    • [j] Ahluwalia, M. & Bull, L. (2001) Coevolving Functions in Genetic Programming. Systems Architecture 47: 573-585.

    top

    Learning Classifier Systems

    • Langdon, W.B. et al. (eds)(2002) Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann. (Founded LCS/GBML track)
    • Bull, L., Lanzi, P-L. & Stolzmann, W. (eds)(2002) Soft Computing: Special Issue on Learning Classifier Systems 6(3-4). Springer.
    • Bull, L. (ed)(2004) Applications of Learning Classifier Systems. Springer.
    • Bull, L. & Kovacs, T. (eds)(2005) Foundations of Learning Classifier Systems. Springer.
    • Bull, L., Bernado Mansilla, E. & Holmes, J. (eds)(2008) Learning Classifier Systems in Data Mining. Springer.
    • Bull, L. & Lanzi, P-L. (eds)(2009) Natural Computing: Special Issue on Learning Classifier Systems 8(1). Springer.
    • Fundamentals
    • Bull, L. (2001) Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-Step Tasks. In P-L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Advances in Learning Classifier Systems: Proceedings of the Third International Workshop. Springer, pp29-36.
    • Bull, L. (2001) Simple Markov Models of the Genetic Algorithm in Classifier Systems: Accuracy-Based Fitness. In P-L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Advances in Learning Classifier Systems: Proceedings of the Third International Workshop. Springer, pp21-28.
    • [j] Bull, L. (2002) On Accuracy-Based Fitness. Soft Computing 6(3-4): 154-161.
    • [j] Bull, L. & Hurst, J. (2002) ZCS Redux. Evolutionary Computation 10(2): 185-205.
    • Hurst, J., Bull, L. & Melhuish, C. (2002) TCS Learning Classifier System Controller on a Real Robot. In J. Merelo, P. Adamidis, H-G. Beyer, J-L. Fernandez-Villacanas & H-P. Schwefel (eds) Parallel Problem Solving from Nature - PPSN VII. Springer Verlag, pp588-600.
    • Bull, L. (2004) A Simple Payoff-based Learning Classifier System. In X. Yao et al. (eds) Parallel Problem Solving from Nature - PPSN VIII. Springer Verlag, pp1032-1041.
    • Kharbat, F., Bull, L. & Odeh, M. (2004) Further Investigation of Accuracy-based Fitness Using a Simple Learning System. In Proceedings of the International Arab Conference on Information Technology (ACIT2004), pp311-318.
    • [b] Bull, L. (2005) Two Simple Learning Classifier Systems. In L. Bull & T. Kovacs (eds) Foundations of Learning Classifier Systems. Springer, pp63-90.
    • Kharbat, F., Bull, L. & Odeh, M. (2005) Revisiting Genetic Selection in the XCS Learning Classifier System. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE, pp2061-2068.
    • Studley, M. & Bull, L. (2005) X-TCS: Accuracy-based Learning Classifier System Robotics. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE, pp2099-2106.
    • Kovacs, T. & Bull, L. (2007) Toward a Better Understanding of Rule Initialisation and Deletion. In Proceedings of the Genetic and Evolutionary Computation Conference Workshop Program. ACM Press, pp2777-2780.
    • Bull, L. (2014) Exploiting Generalisation Symmetries in Accuracy-based Learning Classifier Systems: An Initial Study. In Bull, L. (ed) Evolutionary Computing 20 Symposium: Proceedings of the 50th Anniversary AISB Convention. AISB, pp1-6.
    • [j] Bull, L. (2015) A Brief History of Learning Classifier Systems: From CS-1 to XCS and its Variants. Evolutionary Intelligence 8(2-3): 55-70.
    • Unsupervised Learning: Clustering
    • Tammee, K., Bull, L. & Ouen, P. (2006) A Learning Classifier System Approach to Clustering. In Proceedings of the 6th International Conference on Intelligent Systems Design and Applications. IEEE, pp621-626.
    • Tammee, K., Bull, L. & Ouen, P. (2006) Using a Learning Classifier System for Clustering. In Proceedings of the International Symposium on Communications and Information Technologies 2006. IEEE.
    • [j] Tammee, K., Bull, L. & Ouen, P. (2007) YCSc: A Modified Clustering Technique based on LCS. Journal of Digital Information Management 5(3): 160-167.
    • Tammee, K., Bull, L. & Ouen, P. (2007) Towards Clustering with XCS. In D. Thierens et al. (eds) GECOO-2007: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press, pp1854-1860.
    • [b] Tammee, K., Bull, L. & Ouen, P. (2008) Towards Clustering with Learning Classifier Systems. In L. Bull, E. Bernado-Mansilla & J. Holmes (eds) Learning Classifier Systems in Data Mining. Springer, pp191-204.
    • Bull, L. (2011) Towards a Mapping of Modern AIS and LCS. In P. Lio et al. (eds) Proceedings of the Tenth International Conference on Artificial Immune Systems. Springer, pp371-382 (slides).
    • Cognition: Memory, Lookahead, Latent Learning, and Multiple Objectives
    • Bull, L. & Holland, O. (1994) Internal and External Representations: A Comparison in Evolving the Ability to Count. In Proceedings of the First Annual Society for the Study of Artificial Intelligence and Simulated Behaviour Robotics Workshop. AISB, pp11-14.
    • Bull, L. (2002) Lookahead and Latent Learning in ZCS. In W.B.Langdon, E.Cantu-Paz, K.Mathias, R. Roy, D.Davis, R. Poli, K.Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A.C. Schultz, J. F. Miller, E. Burke & N.Jonoska (eds) GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp897-904.
    • Bull, L. & Studley, M. (2002) Consideration of Multiple Objectives in Neural Learning Classifier Systems. In J. Merelo, P. Adamidis, H-G. Beyer, J-L. Fernandez-Villacanas & H-P. Schwefel (eds) Parallel Problem Solving from Nature - PPSN VII. Springer Verlag, pp549-557.
    • Bull, L. (2004) Lookahead and Latent Learning in a Simple Accuracy-based Learning Classifier System. In X. Yao et al. (eds) Parallel Problem Solving from Nature - PPSN VIII. Springer Verlag, pp1042-1050.
    • [b] Bull, L., Sha'Aban, J., Tomlinson, A., Addison, P. & Heydecker, B. (2004) Towards Distributed Adaptive Control for Road Traffic Junction Signals using Learning Classifier Systems. In L. Bull (ed) Applications of Learning Classifier Systems. Springer, pp276-299.
    • O'Hara, T. & Bull, L. (2005) Building Anticipations in an Accuracy-based Learning Classifier System by use of an Artificial Neural Network. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE Press, pp2046-2052.
    • [j] Studley, M. & Bull, L. (2006) Using the XCS Classifier System for Multi-objective Reinforcement Learning Problems. Artificial Life 13(1): 69-86.
    • Bull, L., Lanzi, P-L. & O'Hara, T. (2007) Anticipation Mappings for Learning Classifier Systems. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE Press, pp2133-2140.
    • Bull, L. (2008) On Lookahead and Latent Learning in Simple LCS. In J. Bacardit, et al. (eds) Learning Classifier Systems: Proceedings of the International Workshops IWLCS 2006 and 2007. Springer, pp154-168.
    • Corporations: Rule-Linkage
    • Tomlinson, A. & Bull, L. (1998) A Corporate Classifier System. In A.E. Eiben, T. Baeck, M. Schoenauer & H-P. Schwefel (eds) Parallel Problem Solving from Nature - PPSN V. Springer Verlag, pp550-559.
    • Tomlinson, A. & Bull, L. (1999) On Corporate Classifier Systems: Improving the use of Rule-Linkage. In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela & R.E. Smith (eds) GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp649-656.
    • Tomlinson, A. & Bull, L. (1999) A Zeroth Level Corporate Classifier System. In A.S. Wu (ed) Proceedings of the Genetic and Evolutionary Computation Conference Workshop Program. Gecco, pp306-313.
    • [b] Tomlinson, A. & Bull, L. (2000) A Corporate XCS. In P-L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Learning Classifier Systems: From Foundations to Applications, Springer, pp194-208.
    • [j] Tomlinson, A. & Bull, L. (2001) Symbiogenesis in Learning Classifier Systems. Artificial Life 7(1):33-62.
    • Tomlinson, A. & Bull, L. (2001) CXCS: Improvements and Corporate Generalizations. In L. Spector et al. (eds) GECCO-2001: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp966-973.
    • [j] Tomlinson, A. & Bull, L. (2002) An Accuracy-Based Corporate Classifier System. Soft Computing 6(3-4): 200-215.
    • Multi-Agent Systems: Pittsburgh and Michigan Styles
    • Bull, L. & Fogarty, T.C. (1993) Coevolving Communicating Classifier Systems for Tracking. In R.F. Albrecht, C.R. Reeves & N.C. Steele (eds) Artificial Neural Networks and Genetic Algorithms. Springer Verlag, pp522-527.
    • Bull, L. & Fogarty, T.C. (1994) Evolving Cooperative Communicating Classifier Systems. In A.V. Sebald & L.J. Fogel (eds) Proceedings of the Third Annual Conference on Evolutionary Programming. World Scientific, pp308-315.
    • Bull, L. & Fogarty, T.C. (1994) Parallel Evolution of Communicating Classifier Systems. In Proceedings of the 1994 IEEE Conference on Evolutionary Computing. IEEE, pp680-685.
    • Bull, L., Fogarty T.C. & Snaith, M. (1995) Evolution in Multi-Agent Systems: Evolving Communicating Classifier Systems for Gait in a Quadrupedal Robot. In L.J. Eshelman (ed) Proceedings of the Sixth International Conference on Genetic Algorithms. Morgan Kaufmann, pp382-388.
    • Bull, L., Fogarty, T.C., Mikami, S., & Thomas, J.G. (1995) Adaptive Gait Acquisition using Multi-agent Learning for Wall Climbing Robots. In Automation and Robotics in Construction XII. IMBibg, pp80-86.
    • [j] Fogarty, T.C. & Bull, L. (1995) Optimising Individual Control Rules and Multiple Communicating Rule-based Control Systems with Parallel Distributed Genetic Algorithms. IEE Journal of Control Theory and Applications 142(3): 211-215.
    • [b] Fogarty, T.C., Bull, L. & Carse, B. (1995) Evolving Multi-Agent Systems. In J. Periaux & G. Winter (eds) Genetic Algorithms in Engineering and Computer Science. John Wiley & Sons, pp3-22.
    • [j] Fogarty, T.C., Carse, B. & Bull, L. (1994) Classifier Systems - recent research. AISB Quarterly (89):48-54.
    • Fogarty, T.C., Carse, B. & Bull, L. (1995) Classifier Systems: selectionist reinforcement learning, fuzzy rules and communication. In Proceedings of the First International Workshop on Biologically Inspired Evolutionary Systems.
    • [b] Fogarty, T.C., Ireson, N.S. & Bull, L. (1995) Genetic-based Machine Learning - Applications in Industry and Commerce. In V.J. Rayward-Smith (ed) Applications of Modern Heuristic Methods. Alfred Waller Ltd, pp91-110.
    • Bull, L. (1998) On ZCS in Multi-Agent Environments. In A.E. Eiben, T. Baeck, M. Schoenauer & H-P. Schwefel (eds) Parallel Problem Solving from Nature - PPSN V. Springer Verlag, pp471-480.
    • Bull, L. (1999) On using ZCS in a Simulated Continuous Double-Auction Market. In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela & R.E. Smith (eds) GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp83-90.
    • Cao, Y.J., Ireson, N., Bull, L. & Miles, R. (2000) Distributed Learning Control of Traffic Signals. In S. Cagnoni, R. Poli, G. Smith, D. Corne, M. Oates, E. Hart, P-L. Lanzi, E. Willem, Y. Li, B. Paecther & T.C. Fogarty (eds) Real-World Applications of Evolutionary Computing: Proceedings of the EvoNet Workshops - EvoSCONDI 2000. Springer, pp117-126.
    • Ireson, N., Cao. Y.J, Bull, L. & Miles, R. (2000) A Communication Architecture for Multi-Agent Learning Systems. In S. Cagnoni, R. Poli, G. Smith, D. Corne, M. Oates, E. Hart, P-L. Lanzi, E. Willem, Y. Li, B. Paecther & T.C. Fogarty (eds) Real-World Applications of Evolutionary Computing: Proceedings of the EvoNet Workshops - EvoTel 2000. Springer, pp255-266.
    • [j] Cao, Y.J., Ireson, N., Bull, L. & Miles, R. (2001) An Evolutionary Intelligent Agents Approach to Traffic Signal Control. International Journal of Knowledge-based Intelligent Engineering Systems 5(4):279-289.
    • Bull, L., Studley, M., Bagnall, A. & Whittley, I. (2005) On the use of Rule Sharing in Learning Classifier System Ensembles. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE, pp612-617.
    • [j] Bull, L., Studley, M., Bagnall, A. & Whittley, I. (2007) Learning Classifier System Ensembles with Rule Sharing. IEEE Transactions on Evolutionary Computation 11(4): 496-502
    • Self-Adaptation
    • Bull, L. & Hurst, J. (2000) Self-Adaptive Mutation in ZCS Controllers. In S. Cagnoni, R. Poli, G. Smith, D. Corne, M. Oates, E. Hart, P-L. Lanzi, E. Willem, Y. Li, B. Paecther & T.C. Fogarty (eds) Proceedings of the EvoNet Workshops: EvoRob. Springer, pp339-346.
    • Bull, L. Hurst, J. & Tomlinson, A. (2000) Self-Adaptive Mutation in Classifier System Controllers. In J-A. Meyer, A. Berthoz, D. Floreano, H. Roitblatt & S.W. Wilson (eds) From Animals to Animats 6 - The Sixth International Conference on the Simulation of Adaptive Behaviour. MIT Press, pp460-468.
    • Hurst, J. & Bull, L. (2001) A Self-Adaptive Classifier System. In P-L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Advances in Learning Classifier Systems: Proceedings of the Third International Workshop. Springer, pp70-79.
    • Hurst, J. & Bull, L. (2002) A Self-Adaptive XCS. In P-L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Advances in Learning Classifier Systems: Proceedings of the Fourth International Workshop on Learning Classifier Systems. Springer, pp57-73.
    • [j] Hurst, J. & Bull, L. (2003) Self-Adaptation in Classifier System Controllers. Artificial Life and Robotics 5(2): 109-119.
    • Real-Valued Representations: Intervals and Fuzzy Logic
    • Cao, Y.J., Ireson, N., Bull, L. & Miles, R. (1999) Design of a Traffic Junction Controller using a Classifier System and Fuzzy Logic. In B. Reusch (ed) Proceedings of the Sixth International Conference on Computational Intelligence Theory and Applications. Springer Verlag, pp342-351.
    • Stone, C. & Bull, L. (2003) Towards Learning Classifier Systems for Continuous-Valued Online Environments. In GECCO-2003: Proceedings of the Genetic and Evolutionary Computation Conference. Springer, pp1924-1925.
    • [j] Stone, C. & Bull, L. (2003) For Real! XCS with Continuous-Valued Inputs. Evolutionary Computation 11(3): 299-336.
    • Casillas, J. Carse, B. & Bull, L. (2004) Fuzzy XCS: an Accuracy-based Fuzzy Classifier System. In Proceedings of the XII Congreso Espanol sobre Tecnologia y Logica Fuzzy (ESTYLF 2004).
    • Wyatt, D., Bull, L. & Parmee, I. (2004) Building Compact Rulesets for Describing Continuous-Valued Problem Spaces Using a Learning Classifier System. In I. Parmee (ed) Adaptive Computing in Design and Manufacture VI. Springer, pp235-248.
    • [b] Stone, C. & Bull, L. (2005) An Analysis of Continuous-Valued Representations for Learning Classifier Systems. In L. Bull & T. Kovacs (eds) Foundations of Learning Classifier Systems. Springer, pp127-178.
    • [j]Casillas, J. Carse, B. & Bull, L. (2007) Fuzzy XCS: a Michigan Genetic Fuzzy System. IEEE Transactions on Fuzzy Systems 15(4): 536-550.
    • Kharbat, F., Bull, L. & Odeh, M. (2007) Mining Breast Cancer Data with XCS. In D. Thierens et al. (eds) GECOO-2007: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press, pp2066-2073.
    • [j] Kharbat, F., Odeh, M. & Bull, L. (2007) New Approach for Extracting Knowledge from XCS Learning Classifier Systems. International Journal of Hybrid Intelligent Systems 4(2): 49-62.
    • Wyatt, D., Bull, L. & Parmee, I. (2007) Applying XCSR to Design-Orientated Environments. In T. Kovacs, X. Llora, K. Takadama, P-L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Learning Classifier Systems: International Workshops IWLCS 2003-2005. Springer, pp318-342.
    • [b] Kharbat, F., Bull, L. & Odeh, M. (2008) Knowledge Discovery from Medical Data: An Empirical Study with XCS. In L. Bull, E. Bernado-Mansilla & J. Holmes (eds) Learning Classifier Systems in Data Mining. Springer, pp93-122.
    • [b] Stone, C. & Bull, L. (2008) Foreign Exchange Trading using a Learning Classifier System. In L. Bull, E. Bernado-Mansilla & J. Holmes (eds) Learning Classifier Systems in Data Mining. Springer, pp169-190.
    • [j] Kharbat, F., Odeh, M. & Bull, L. (2013) A New Hybrid Architecture for the Discovery and Compaction of Knowledge: Breast Cancer Datasets Case Study. International Arab Journal of Information Technology
    • Genetic Programming
    • Ahluwalia, M. & Bull, L. (1999) A Genetic Programming-based Classifier System. In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela & R.E. Smith (eds) GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp11-18.
    • [j] Bull, L. (2009) On Dynamical Genetic Programming: Simple Boolean Networks in Learning Classifier Systems. International Journal of Parallel, Emergent and Distributed Systems 24(5): 421-442
    • Bull, L. & Preen, R. (2009) On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems. In L. Vanneschi et al. (eds) Proceedings of the Twelfth European Conference on Genetic Programming. Springer, pp37-48.
    • Preen, R. & Bull, L. (2009) Discrete Dynamical Genetic Programming in XCS. In GECCO-2009: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press.
    • Preen, R. & Bull, L. (2011) Fuzzy Dynamical Genetic Programming in XCSF. In GECCO-2011: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press.
    • Preen, R. & Bull, L. (2011) Arithmetic Dynamical Genetic Programming in the XCSF Learning Classifier System. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE Press.
    • [j] Preen, R. & Bull, L. (2013) Dynamical Genetic Programming in XCSF. Evolutionary Computation 21(3): 361-388.
    • [j] Preen, R. & Bull, L. (2014) Discrete and Fuzzy Dynamical Genetic Programming in the XCSF Learning Classifier System. Soft Computing 18(1): 153-167.
    • Neural Learning Classifier Systems
    • Bull, L. (2002) On Using Constructivism in Neural Classifier Systems. In J. Merelo, P. Adamidis, H-G. Beyer, J-L. Fernandez-Villacanas & H-P. Schwefel (eds) Parallel Problem Solving from Nature - PPSN VII. Springer Verlag, pp558-567.
    • Bull, L. & O'Hara, T. (2002) Accuracy-based Neuro and Neuro-Fuzzy Classifier Systems. In W.B.Langdon, E.Cantu-Paz, K.Mathias, R. Roy, D.Davis, R. Poli, K.Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A.C. Schultz, J. F. Miller, E. Burke & N.Jonoska (eds) GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp905-911.
    • Bull, L. & Hurst, J. (2003) A Neural Learning Classifier System with Self-Adaptive Constructivism. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE Press, pp991-997.
    • Hurst, J. & Bull, L. (2004) A Self-Adaptive Neural Learning Classifier System with Constructivism for Mobile Robot Control. In X. Yao et al. (eds) Parallel Problem Solving from Nature - PPSN VIII. Springer Verlag, pp942-951.
    • [j] Hurst, J. & Bull, L. (2006) A Neural Learning Classifier System with Self-Adaptive Constructivism for Mobile Robot Control. Artificial Life 12(3): 353-380.
    • Howard, D., Bull, L. & Lanzi, P-L. (2008) Self-Adaptive Constructivism in Neural XCS and XCSF. In M. Keijzer et al. (eds) GECCO-2008: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press, pp1389-1396.
    • Howard, D. & Bull, L. (2008) On the Effects of Node Duplication and Connection-Orientated Constructivism in Neural XCSF. In M. Keijzer et al. (eds) GECCO-2008: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press, pp1977-1984.
    • Howard, D., Bull, L. & Lanzi, P-L. (2009) Continuous Actions in Continuous Space and Time using Self-Adaptive Constructivism in Neural XCSF. In GECCO-2009: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press.
    • Howard, D., Bull, L. & Lanzi, P-L. (2010) A Spiking Neural Representation for XCSF. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE Press.
    • Howard, D., Bull, L. & Lanzi, P-L. (2010) Use of a Connection-Selection Scheme in Neural XCSF. In J. Bacardit, et al. (eds) Learning Classifier Systems: Proceedings of the International Workshops IWLCS 2008 and 2009. Springer, pp87-106.
    • [j] Howard, D., Bull, L. & Lanzi, P-L. (2016) A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers. Neural Processing Letters 44(1):125-147.

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    Artificial Life

    • [j] Bull, L. (1996) Artificial Life: An Overview, by C.G. Langton. Expert Systems 13(1):63
    • Adamatzky, A., Bull, L., De Lacy Costello, B.,Stepney, S. & Teuscher, C. (eds)(2007) Unconventional Computing 2007. Luniver Press.
    • [j] Bull, L. (2013) A Computer Scientist’s View on Mobile DNA - Comment on “How Life Changes Itself: The Read-Write (RW) Genome” by James Shapiro. Physics of Life Reviews 10(3): 326-327
    • Symbiogenesis
    • Bull, L., Fogarty, T.C. & Pipe, A.G. (1995) Artificial Endosymbiosis. In F. Moran, A. Mereno, J.J. Merelo & P. Chaon (eds) Advances in Artificial Life - Proceedings of the Third European Conference on Artificial Life. Springer Verlag, pp273-289.
    • Bull, L. & Fogarty, T.C. (1996) Horizontal Gene Transfer in Endosymbiosis. In C.G. Langton & T. Shimohara (eds) Artificial Life V. MIT Press, pp77-84.
    • [j] Bull, L. & Fogarty, T.C. (1996) Artificial Symbiogenesis. Artificial Life 2(3):269-292.
    • [b] Bull, L. (1999) On the Evolution of Eukaryotes: Computational Models of Symbiogenesis and Multicellularity. In C. Nehaniv (ed) Mathematical and Computational Biology: Hierarchical Complexity and Digital Evolution. American Mathematical Society, pp31-46.
    • [b] Bull, L. & Tomlinson, A. (2004) Symbiogenesis in Machine Learning. In R. Paton (ed) Computation in Cells and Tissues: Perspectives and Tools of Thought. Springer, pp27-50.
    • [j] Bull, L. (2010) Artificial Symbiogenesis and Differing Reproduction Rates. Artificial Life 16(1): 65-72.
    • Random Boolean Networks (see also here )
    • [j] Alonso-Sanz, R. & Bull, L. (2008) Boolean Networks with Memory. Bifurcation and Chaos 18(12): 3799-3814
    • Bull, L. & Alonso-Sanz, R. (2008) On Coupling Random Boolean Networks. In Adamatzky et al. (eds) Automata 2008: Theory and Applications of Cellular Automata. Luniver Press, pp292-301.
    • [j] Alonso-Sanz, R. & Bull, L. (2009) On Minimally Coupled Boolean Networks. Bifurcation and Chaos 19(4): 1401-1414
    • [j] Bull, L. (2012) Evolving Boolean Networks on Tunable Fitness Landscapes. IEEE Transactions on Evolutionary Computation 16(6): 817-828.
    • [j] Bull, L. (2012) A Simple Computational Cell: Coupling Boolean Gene and Protein Networks. Artificial Life 18(2): 223-236.
    • [j] Bull, L. (2012) Production System Rules as Protein Complexes from Genetic Regulatory Networks: An Initial Study. Evolutionary Intelligence 5(2): 59-67
    • [j] Bull, L. (2012) Evolving Boolean Networks with Structural Dynamism. Artificial Life 18(4): 385-398
    • [j] Bull, L. (2013) Consideration of Mobile DNA: New Forms of Artificial Genetic Regulatory Networks. Natural Computing 12(4): 443-452.
    • Bull, L. & Adamatzky, A. (2013) Evolving Gene Regulatory Networks with Mobile DNA Mechanisms. In Y. Jin et al. (eds) Proceedings of the UK Workshop on Computational Intelligence. IEEE Press, pp1-7.
    • [j] Bull, L. (2014) Evolving Boolean Regulatory Networks with Epigenetic Control. BioSystems 116: 36-42
    • [j] Bull, L. (2014) Evolving Functional and Structural Dynamism in Coupled Boolean Networks. Artificial Life 20 (4): 441-455
    • [j] Bull, L. (2016) On the Evolution of Boolean Networks for Computation: A Guide RNA Mechanism. International Journal of Parallel, Emergent and Distributed Systems 31(2): 101-113
    • Design Mining: Artificial Creativity, Surrogate Models, and 3D Printing
    • Bull, L. (1997) Model-based Evolutionary Computing: A Neural Network and Genetic Algorithm Architecture. In Proceedings of the 1997 IEEE Conference on Evolutionary Computing. IEEE, pp611-616.
    • [j] Bull, L. (1999) On Model-based Evolutionary Computing. Soft Computing 3(2):183-190.
    • Bull, L., Wyatt, D. & Parmee, I. (2002) Initial Modifications to XCS for use in Interactive Evolutionary Design. In J. Merelo, P. Adamidis, H-G. Beyer, J-L. Fernandez-Villacanas & H-P. Schwefel (eds) Parallel Problem Solving from Nature - PPSN VII, Springer Verlag, pp568-577.
    • Bull, L., Wyatt, D. & Parmee, I. (2002) Towards the use of XCS for Interactive Evolutionary Design. In W.B.Langdon, E.Cantu-Paz, K.Mathias, R. Roy, D.Davis, R. Poli, K.Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A.C. Schultz, J. F. Miller, E. Burke & N.Jonoska (eds) GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp951.
    • Bull, L. (2008) Toward Artificial Creativity with Evolution Strategies. In I. Parmee (ed) Adaptive Computing in Design and Manufacture VIII. IPCC.
    • Preen, R. & Bull, L. (2013) Towards the Evolution of Novel Vertical-Axis Wind Turbines. In Y. Jin et al. (eds) Proceedings of the UK Workshop on Computational Intelligence. IEEE Press, pp74-81. (slides).
    • Preen, R. & Bull, L. (2014) Towards the Evolution of Vertical-Axis Wind Turbines using Supershapes. In Bull, L. (ed) Evolutionary Computing 20 Symposium: Proceedings of the 50th Anniversary AISB Convention. AISB, pp15-22.
    • [j] Preen, R. & Bull, L. (2014) Towards the Evolution of Vertical-Axis Wind Turbines using Supershapes. Evolutionary Intelligence 7(3): 155-167.
    • [j] Preen, R. & Bull, L. (2015) Towards the Coevolution of Novel Vertical-Axis Wind Turbines. IEEE Transactions on Evolutionary Computation 19(2): 284-294 (NB paper was picked up on by Motherboard and others).
    • [j] Preen, R. & Bull, L. (2016) Design Mining Interacting Wind Turbines. Evolutionary Computation 24(1):89-111.
    • [j] You, J. Ieropoulos, I., Preen, R., Bull, L. & Greenman, J. (2017) 3D Printed Components of Microbial Fuel Cells: Towards Monolithic Microbial Fuel Cell Fabrication using Additive Layer Manufacturing. Sustainable Energy Technology and Assesments (in press)
    • Baldwin Effect: Evolution and Learning
    • Bull, L. & Fogarty, T.C. (1994) An Evolution Strategy and Genetic Algorithm Hybrid: An Initial Implementation and First Results. In T.C. Fogarty (ed) Evolutionary Computing. Springer Verlag, pp95-102.
    • Bull, L. (1997) On the Evolution of Multicellularity. In P. Husbands & I. Harvey (eds) Proceedings of the Fourth European Conference on Artificial Life. MIT Press, pp190-196.
    • [j] Bull, L. (1999) On the Evolution of Multicellularity and Eusociality. Artificial Life 5(1):1-15.
    • [j] Bull, L. (1999) On the Baldwin Effect. Artificial Life 5(3):241-246.
    • [b]Wyatt, D. & Bull, L. (2004) A Memetic Learning Classifier System for Describing Continuous-Valued Problem Spaces. In N. Krasnagor, W. Hart & J. Smith (eds) Recent Advances in Memetic Algorithms. Springer, pp355-396.
    • O'Hara, T. & Bull, L. (2005) A Memetic Accuracy-based Neural Learning Classifier System. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE Press, pp2040-2045.
    • O'Hara, T. & Bull, L. (2007) Backpropagation in Accuracy-based Neural Learning Classifier Systems. In T. Kovacs, X. Llora, K. Takadama, P-L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Learning Classifier Systems: International Workshops IWLCS 2003-2005. Springer, pp26-40.
    • [j] Bull, L. (2015) On Cellular Darwinism: Mitochondria. Artificial Life 22(1):112-118.
    • Cellular Automata (see also here )
    • Bull, L., Lawson, I., Adamatzky, A. & DeLacyCostello, B. (2005) Towards Predicting Spatial Complexity: A Learning Classifier System Approach to Cellular Automata Identification. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE, pp136-141.
    • [j] Bull, L. & Adamatzky (2007) A Learning Classifier System Approach to the Identification of Cellular Automata. Cellular Automata 2(1): 21-38.
    • [j] Alonso-Sanz, R. & Bull, L. (2008) Random Number Generation by Cellular Automata with Memory. International Journal of Modern Physics C 19(2): 351-367.
    • Alonso-Sanz, R. & Bull, L. (2008) Elementary Coupled Cellular Automata with Memory. In Adamatzky et al. (eds) Automata 2008: Theory and Applications of Cellular Automata. Luniver Press, pp72-96.
    • [j] Alonso-Sanz, R. & Bull, L. (2009) A Very Effective Density Classifier Two-Dimensional Cellular Automaton with Memory. Journal of Physics A 42(48):
    • [j] Stone, C. & Bull, L. (2009) Solving the Density Classification Task with Cellular Automaton 184 and Memory. Complex Systems 18(3): 329-344.
    • [j] Alonso-Sanz, R. & Bull, L. (2010) One-Dimensional Coupled Cellular Automata with Memory: Initial Investigations. Cellular Automata 5 (1-2): 29-49
    • Chemical Computation
    • Budd, A., Stone, C., Masere, J., Adamatzky, A., DeLacyCostello, B. & Bull, L. (2006) Towards Machine Learning Control of Chemical Computers. In A. Adamatzky & C. Teuscher (eds) From Utopian to Genuine Unconventional Computers. Luniver Press, pp17-36.
    • Stone, C., Toth, R., Adamatzky, A., Bull, L. & De Lacy Costello, B. (2007) Towards the Coevolution of Cellular Automata Controllers for Chemical Computing with the B-Z Reaction. In D. Thierens et al. (eds) GECOO-2007: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press, pp472-478.
    • [j] Budd, A., Stone, C., Masere, J., Adamatzky, A., DeLacyCostello, B. & Bull, L. (2008) Initial Results from the use of Evolutionary Learning to Control Chemical Computers. Unconventional Computing 4(1): 13-22.
    • Stone, C., Toth, R., De Lacy Costello, B., Adamatzky, A. & Bull, L. (2008) Coevolving Cellular Automata with Memory for Chemical Computing: Boolean Logic Gates in the B-Z Reaction. In G. Rudolph et al. (eds) Parallel Problem Solving from Nature - PPSN X. Springer, pp579-588.
    • [j] Taylor, A., Kapetanopoulos, P., Whitaker, B., Toth, R., Bull, L. & Tinsley, M. (2008) Clusters and Switchers in Globally Coupled Photochemical Oscillators. Physical Review Letters 100(21)
    • Toth, R., Stone, C., De Lacy Costello, B., Adamatzky, A. & Bull, L. (2008) Towards Designing Collision-based Chemical Logic Gates with Adaptive Computing. In I. Parmee (ed) Adaptive Computing in Design and Manufacture VIII. IPCC.
    • [j] Toth, R., Stone, C., De Lacy Costello, B., Adamatzky, A. & Bull, L. (2008) Dynamic Control and Information Processing in the Belousov-Zhabotinsky Reaction using a Co-evolutionary Algorithm. Journal of Chemical Physics 129: 184708.
    • [j] Adamatzky, A. & Bull, L. (2009) Are Complex Systems Hard to Evolve? Complexity 14(6): 15-20.
    • [j] De Lacy Costello, B., Toth, R., Stone, C., Adamatzky, A. & Bull, L. (2009) Implementation of Glider Guns in the Light-Sensitive Belousov-Zhabotinsky Medium. Physical Review E 79(2).
    • [j] Toth, R., Stone, C., De Lacy Costello, B., Adamatzky, A. & Bull, L. (2009) Experimental validation of binary collisions between wave fragments in the photosensitive Belousov-Zhabotinsky reaction. Chaos, Solitons & Fractals 41(4): 1605-1615.
    • [j] Toth, R., Stone, C., De Lacy Costello, B., Adamatzky, A. & Bull, L. (2009) Simple Collision-based Chemical Logic Gates with Adaptive Computing. Journal of Nanotechnology and Molecular Computation 1(3): 1-16.
    • [j] Toth, R., De Lacy Costello, B., Stone, C., Adamatzky, A. & Bull, L. (2010) Spiral Formation and Degeneration in Heterogeneous Excitable Media. Physical Review E (in press).
    • [j] Adamatzky, A., De Lacy Costello, B., Holley, J., Gorecki, J. & Bull, L. (2011) Vesicle computers: Approximating a Voronoi diagram on Voronoi automata. Chaos, Solitons & Fractals 44: 480-489.
    • [j] Adamatzky, A., De Lacy Costello, B. & Bull, L. (2011) On Polymorphic Logical Gates in Sub-Excitable Chemical Medium. Bifurcation and Chaos 21(7): 1977-1986.
    • [j] Adamatzky A., Holley J., Bull L. & De Lacy Costello B. (2011) On Computing in Fine-Grained Compartmentalised Belousov-Zhabotinsky Medium. Chaos, Solitons & Fractals (in press)
    • [j] Holley, J., Adamatzky, A., Bull, L. De Lacy Costello, B., & Jahan, I. (2011) Computational modalities of Belousov-Zhabotinsky encapsulated vesicles. Nano Networks 2: 50-61.
    • [j] Holley, J., Jahan, I., De Lacy Costello, B., Adamatzky, A. & Bull, L. (2011) Logical and arithmetic circuits in Belousov-Zhabotinsky encapsulated discs. Physical Review E (in press)
    • [j] Adamatzky, A., Holley, J., Dittrich, P., Gorecki, J., De Lacy Costello, B., Zauner, K-P & Bull, L. (2012) On architectures of circuits implemented in simulated Belousov-Zhabotinsky droplets. Biosystems 109(1): 72-77.
    • [b] Bull, L., Holley, J., De Lacy Costello, B. & Adamatzky, A. (2013) Toward Turing's A-type Unorganised Machines in an Unconventional Substrate: a Dynamic Representation in Compartmentalised Excitable Chemical Media. In G. Dodig-Crnkovic & R. Giovagnoli (eds) Computing Nature: Turing Centenary Perspective. Springer, pp185-200.
    • [j] De Lacy Costello, B., Jahan, I., Ahearn, M., Holley, J., Bull, L. & Adamatzky, A. (2013) Initiation of Waves in BZ Encapsulated Vesicles Using Light - Towards Design of Computing Architectures. Unconventional Computing 9(3-4): 311-326.
      Imitation
    • [j] Bull, L., Holland, O. & Blackmore, S.(2000) On Meme-Gene Coevolution. Artificial Life 6(3): 227-235.
    • Bull, L. (2010) Imitation Programming. In G. Tempesti et al. (eds) Proceedings of the 9th International Conference on Evolvable Systems - From Biology to Hardware. Springer, pp360-371 (slides).
    • Erbas, M., Winfield, A. & Bull, L. (2011) Towards Imitation-enhanced Reinforcement Learning in Multi-Agent Systems. In Proceedings of the IEEE Symposium on Artificial Life. IEEE.
    • [j] Bull, L. (2012) Using Genetical and Cultural Search to Design Unorganised Machines. Evolutionary Intelligence 5(1): 23-34
    • [b] Bull, L. (2013) Imitation Programming Unorganised Machines. In X. Yang (ed) Artificial Intelligence, Evolutionary Computation and Metaheuristics: In the footsteps of Alan Turing. Springer.
    • [j] Erbas, M., Winfield, A. & Bull, L. (2014) Embodied Imitation-Enhanced Reinforcement Learning in Multi-Agent Systems. Adaptive Behaviour 22(1): 31-50.
    • [j] Erbas, M., Winfield, A. & Bull, L. (2015) On the Evolution of Behaviors through Embodied Imitation. Artificial Life 21(2): 141-165.
    • Neuronal Computation
    • Uroukov, I., Ma, M., Bull, L. & Purcell, W. (2006) MEA Recordings of the Spontaneous Behaviour of Hen Embryo Brain Spheroids. In Proceedings of the 5th International Meeting on Substrate-Integrated Micro Electrode Arrays. BIOPRO, pp232-234.
    • [j] Uroukov, I., Ma, M., Bull, L. & Purcell, W. (2006) Electrophysiological Measurements in 3-Dimensional In Vivo-Mimetic Organotypic Cell Cultures: Preliminary Studies with Hen Embryo Brain Spheroids. Neuroscience Letters 404: 33-38
    • Bull, L. & Uroukov, I. (2007) Initial Results from the use of Learning Classifier Systems to Control In Vitro Neuronal Networks. In D. Thierens et al. (eds) GECOO-2007: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press, pp369-376.
    • [j] Bull, L. & Uroukov, I. (2008) Towards Neuronal Computing: Simple Creation of Two Logic Functions in 3D Cell Cultures using Multi-Electrode Arrays. Unconventional Computing 4(2): 143-154.
    • [j] Bull, L., Budd, A., Stone, C., Uroukov, I., De Lacy Costello, B. & Adamatzky, A. (2008) Towards Unconventional Computing Through Simulated Evolution: Learning Classifier System Control of Non-Linear Media. Artificial Life 14(2): 203-222.
    • Uroukov, I. & Bull, L. (2008) Mapping The Impact Of Long-Term Electrical Stimulation To Organotypical Hen Embryonic Brain (HEB) Spheroids On Their Spiking Activity. Part I. In Proceedings of the 6th International Meeting on Substrate-Integrated Micro Electrode Arrays. BIOPRO.
    • Uroukov, I. & Bull, L. (2008) Mapping The Impact Of Long-Term Electrical Stimulation To Organotypical Hen Embryonic Brain (HEB) Spheroids On Their Spiking Activity. Part II. In Proceedings of the 6th International Meeting on Substrate-Integrated Micro Electrode Arrays. BIOPRO.
    • [j] Uroukov, I. & Bull, L. (2008) On the Effect of Long-Term Electrical Stimulation on 3-Dimensional Cell Cultures: Hen Embryo Brain Spheroids. Medical Devices: Evidence and Research 1: 1-12.
    • [j] Miranda, E.R., Bull, L., Gueguen, F. & Uroukov, I. (2009) Computer Music Meets Unconventional Computing: Towards Sound Synthesis with In Vitro Neuronal Networks. Computer Music Journal 33(1): 9-18.
    • Memristors
    • Howard, D., Gale, E., Bull, L., De Lacy Costello, B. & Adamatzky, A. (2011) Towards Evolving Spiking Networks with Memristive Synapses. In Proceedings of the IEEE Symposium on Artificial Life. IEEE press.
    • Howard, D., Gale, E., Bull, L., De Lacy Costello, B. & Adamatzky, A. (2011) Evolving Spiking Networks with Variable Memristor Synapses. In GECCO-2011: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press.
    • [j] Howard, D., Gale, E., Bull, L., De Lacy Costello, B. & Adamatzky, A. (2012) Evolution of Plastic Learning in Spiking Networks via Memristive Connections. IEEE Transactions on Evolutionary Computation 16(5): 711-729
    • Howard, D., Bull, L. & Adamatzky, A. (2012) Cartesian Genetic Programming for Memristive Logic Circuits. In Proceedings of the Fifteenth European Conference on Genetic Programming. Springer.
    • [j] Howard, D., Bull, L., De Lacy Costello, B., Adamatzky, A. and Erokhin, V. (2013) A SPICE Model of the PEO-PANI Memristor. International Journal of Bifurcation and Chaos 23(6).
    • [j] Howard, D., Bull, L., De Lacy Costello, B. & Adamatzky, A. (2013) Creating Unorganised Machines from Memristors. International Journal of Applied Mathematics and Information Sciences 7(4): 1275-1283.
    • [j] Howard, D., Bull, L., De Lacy Costello, B., Gale, E. & Adamatzky, A. (2014) Evolving Spiking Networks with Variable Memristive Memories. Evolutionary Computation 22(1): 79-103 (NB made Computing Review Notable Articles list for 2014).
    • Howard, D., Bull, L. & De Lacy Costello, B. (2015) Evolving Unipolar Memristor Spiking Neural Networks. In Artificial Life and Computational Intelligence. Springer, pp258-272.
    • [j] Howard, D., Bull, L. & De Lacy Costello, B. (2015) Evolving Unipolar Memristor Spiking Neural Networks. Connection Science 27(4):397-416

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    Other (Data Mining)

    • Tekiner, F., Pettipher, M, Bull, L. & Bagnall, A. (eds)(2007) Mediterranean Journal of Computers and Networks: Special Issue on Data Mining in Supercomputer and Grid Environments. SoftMotor.
      Non-Evolutionary: Ensembles and Features
    • Bagnall, A., Whittley, I., Studley, M., Tekiner, F., Pettipher, M. & Bull, L. (2006) Variance Stabilizing Regression Ensembles for Environmental Models. In Proceedings of the IEEE International Joint Conference on Neural Networks. IEEE Press, pp5355-5361.
    • Bagnall, A., Whittley, I., Janacek, G., Kemsley, K., Studley, M. & Bull, L. (2006) A Comparison of DWA/PAA and DFT for Time Series Classification. In Proceedings of the 2006 International Conference on Data Mining. CSREA Press, pp403-409.
    • Whittley, I.M., Bagnall, A.J., Bull, L., Pettipher, M., Studley, M. & Tekiner, F. (2006) Attribute Selection Methods for Filtered Attribute Subspace based Bagging with Injected Randomness (FASBIR). In Feature Selection for Data Mining Workshop, Part of the 2006 SIAM Conference on Data Mining.
    • [j] Bagnall, A., Cawley, G., Whittley, I., Bull, L., Studley, M, Tekiner, F. & Pettipher, M. (2007) Super Computer Heterogeneous Classifier Meta-Ensembles. International Journal of Data Warehousing and Mining 3(2): 67-82
    • [b] Bagnall, A., Cawley, G., Whittley, I., Bull, L., Studley, M, Tekiner, F. & Pettipher, M. (2008) Super Computer Heterogeneous Classifier Meta-Ensembles. In J. Wang (ed) Data Warehousing and Mining: Concepts, Methodologies, Tools and Applications. IGI Global, pp1320-1333.
    • Genetic Programming
    • Smith, M. & Bull, L. (2003) Feature Construction and Selection using Genetic Programming and a Genetic Algorithm. In C. Ryan, T. Soule, E. Tsang, R. Poli & E. Costa (eds) Genetic Programming: Proceedings of 6th European Conference, EuroGP 2003. Springer, pp229-237.
    • [b] Smith, M. & Bull, L. (2004) GAP: Constructing and Selecting Features with Evolutionary Computing. In A. Gosh & L. Jain (eds) Evolutionary Computation in Data Mining. Springer, pp41-56.
    • Smith, M. & Bull, L. (2004) Using Genetic Programming for Feature Creation with a Genetic Algorithm Feature Selector. In X. Yao et al. (eds) Parallel Problem Solving from Nature - PPSN VIII. Springer Verlag, pp1163-1171.
    • [j] Smith, M. & Bull, L. (2005) Genetic Programming with a Genetic Algorithm for Feature Construction and Selection. Genetic Programming and Evolvable Machines 6(3): 265-281.
    • Smith, M. & Bull, L. (2007) Improving the Human Readability of Features Constructed by Genetic Programming. In D. Thierens et al. (eds) GECOO-2007: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press, pp1694-1701.


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PhD Students

  • Dom Brown (current) - Gestural Languages for Gestural Musical Instruments
  • Sam Hunt (current) - Music Content Analysis and Digitally Supported Musical Creativity
  • Paul Rendell (2013) - Turing Machines in the Game of Life
  • Jeff Jones (2012) - Multi-agent Modelling of Physarum polycephalum
  • Mehmet Erbas (2012) - Imitation and Learning in Collective Robot Systems
  • Richard Preen (2011) - Dynamical Genetic Programming Learning Classifier Systems
  • David Howard (2010) - Constructionist and Spiking Neural Learning Classifier Systems
  • Matt Smith (2009) - Using Genetic Programming for Feature Creation with a Genetic Algorithm Feature Selector
  • Andrea Staggemeier (2008) - Metaheuristics in a Production Lot-Sizing and Scheduling Problem
  • Johnson Abraham (2007) - Multi-objective Information Generation and Presentation within Interactive Evolutionary Design and Decision Making Systems
  • Toby O'Hara (2006) - Neural Representations in Learning Classifier Systems
  • Faten Kharbat (2006) - Learning Classifier Systems for Knowledge Discovery in Breast Cancer
  • Matt Studley (2005) - Learning Classifier Systems for Multi-objective Robot Control
  • Christopher Stone (2005) - Learning Classifier Systems for Decision Making in Continuous-Valued Domains
  • David Wyatt (2004) - Applying the XCS Learning Classifier System to Continuous-Valued Data Mining Problems
  • Claudio Bonacina (2003) - Evolutionary Computing in Multi-Agent Systems
  • Jacob Hurst (2002) - Learning Classifier Systems in Robotic Environments
  • Natalio Krasnogor (2002) - Studies on the Theory and Design Space of Memetic Algorithms
  • Manu Ahluwalia (2000) - Coevolving Functions in Genetic Programming
  • Andy Tomlinson (1999) - Corporate Classifier Systems

Projects

  • Design Mining: A Microbial Fuel Cell Pilot Study (EPSRC: Jiseon You, Richard Preen, 2015-2017)
    • Use of machine learning and 3D printing within the engineering design process to exploit novel materials/technology and enhance creativity under an agile-like system, with Ioannis Ieropoulos and John Greenman.

  • Embodied Evolutionary Computing Design: Vertical Axis Wind Turbine Case Study (Leverhulme Trust: Richard Preen, 2014-2015)
    • Use of evolutionary computing to design hard to formulate/simulate physical systems through 3D printing.

  • Biologically inspired transportation: a distributed intelligent conveyor (EPSRC: Ioannis Georgilas, 2011-2013)
    • Creation of a cilia-inspired smart surface and evolutionary design of appropriate controllers, with Andy Adamatzky (PI), in collaboration with Manchester.

  • Learning and Computation in Disordered Networks of Memristors: Theory and Experiments (EPSRC: David Howard, Ella Gale, 2010-2013)
    • Creation and use through evolutionary design of memristors, with Andy Adamatzky (PI) and Ben De Lacy Costello.

  • NEUNEU (EU: Julian Holley, 2010-2013)
    • In collaboration with the Universities of Cardinal Stefan Wyszynski, Friedrich Schiller in Jena, and Southampton, the evolutionary design of chemical neural networks - with Andy Adamatzky (PI) and Ben De Lacy Costello.

  • HEAT@UWE: Bridging the gaps in Health, Environment And Technology (HEAT) research (EPSRC: 2009-2012)
    • Initiative to form new multi-disciplined research themes across UWE, led by Katie Williams (PI).

  • Discrete Dynamical Systems with Memory: A New Tool for Modelling Complexity (EPSRC: Ramon Alonso-Sanz, Chris Stone, 2007-2009)
    • Exploring the use of memory in systems such as cellular automata, with Andy Adamatzky.

  • Mining Olympic Sailing Boat Telemetry Data (EPSRC: Julian Holley, 2007-2008)
    • Using machine learning to explore Olympic team data, in collaboration with UK Sport.

  • Machine Learning Mining of Cricket Test Data (English Cricket Board: Consultancy, 2007-2008)
    • Using machine learning to explore match data.

  • Mining Athlete Event Data (EPSRC: Ian Whittley, 2007-2008)
    • Using machine learning to explore Olympic athlete data, in collaboration with UK Sport.

  • Evolutionary Design of Collison-based Computing Schemes in Two-dimensional Cellular Automata (EPSRC: Emmanuel Sapin, 2006-2007)
    • Exploring the use of genetic algorithms to design architecture-less computers, with Andy Adamatzky (PI).

  • AJ-SINIC: Anglo-Japanese Initiative in Non-Linear Media Computing (EPSRC: 2005-2006)
    • Establishment of a collaborative network in non-classical computation based on principles of information processing in physical and chemical media, with Andy Adamatzky (PI) and Ben DeLacyCostello.

  • Non-Linear Media Based Computers (EPSRC: Mingwen Ma, Chris Stone, Rita Toth and Ivan Uroukov, 2004-2008)
    • Novel Computation project to develop and test a new approach to enable desired computations/behaviour from networks of non-linear media, in collaboration with Sussex and Leeds.

  • Super-Computer Data Mining (EPSRC: Matthew Studley, 2004-2006)
    • Creation of a UK machine learning data mining tool, in collaboration with Manchester and UEA.

  • Cluster on Non-Linear Media Based Computers (EPSRC: 2003-2004)
    • Novel Computation cluster, in collaboration with Andy Adamatzky (PI) and Ben DeLacyCostello.

  • A Learning Classifier System Approach to Neural Constructivism (EPSRC ROPA: Jacob Hurst, 2002-2004)
    • Use of LCS to control mobile robots where each rule is a neural network and their complexity emerges during learning.

  • Data Mining (LloydsTSB: Praminda Caleb-Solly, 2001-2003)
    • Project to use a number of machine learning techniques for analysis of various types of financial data.

  • Distributed Adaptive Control for Road Traffic Systems (EPSRC: Andy Tomlinson, 2001-2003)
    • A project applying LCS to road traffic signal control in collaboration with the traffic group at UCL.

  • Evolutionary Robotics (BT Labs & UWE CollR: Jacob Hurst, 1999-2002)
    • A project using self-adaptive LCS for learning in mobile robot systems.

  • Vintage (EU ESPRIT: YiJa Cao and Neil Ireson, 1998-2000)
    • A European project which built a distributed LCS kernel, with applications in distributed control and scheduling.

  • Adaptive Economic Trading Agents (HP Labs: myself, 1998-1999)
    • A one-year project to examine the use of learning agents in a simulated continuous double-auction market.


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Contact

    Room Number : 3Q17
    E-mail address : larryDOTbullATuwe.ac.uk
    Phone Number : +44 (0) 117 3283161
    Mail Address :-
                    Department of Computer Science & Creative Technologies,
                    The University of the West of England,
                    Frenchay,
                    BRISTOL
                    BS16 1QY
                    U.K.


Past