Larry Bull

Founding Editor-in-Chief (2007-17) Evolutionary Intelligence, Springer

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 Unpublished.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.
• [j] Bull, L. (2020) Exploring Distributed Control with the NK Model. International Journal of Parallel, Emergent and Distributed Systems 35(4): 413-422
• [j] Bull, L. (2021) On Coevolution: Asymmetry in the NKCS Model. BioSystems 207:
• [j] Bull, L. (2022) Non-Binary Representations in the NK and NKCS Models. Complex Systems 31(1): 87-101
• [j] Bull, L. & Lui, H.(2022) A Generalised Drop-Out Mechanism for Distributed Systems. Artificial Life (in press)

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]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.
• [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.
• Kovacs, T., Rawles, S., Bull, L., Nakata, M. & Takadama, K. (2016) DH-XCS: Minimal Default Hierarchies in XCS. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE, pp4747-4754.

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.
• 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.
• [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.
• 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.
• 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.
• [j] Preen, R., Wilson, S.W. & Bull, L. (2021) Autoencoding with a Classifier System. IEEE Transactions on Evolutionary Computation 25(6): 1079-1090

top



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
• Bull, L. (2020) The Evolution of Complexity: Simple Simulations of Major Innovations. Springer.

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.
• [j] Bull, L. (2015) On Cellular Darwinism: Mitochondria. Artificial Life 22(1):112-118.

Gene Regulatory 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 Tuneable 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
• [b] Bull, L. (2021) Evolving Gene Regulatory Networks with Variable Gene Expression Times. In A. Adamatzky et al. (eds) Handbook of Unconventional Computing Vol. 1. Springer, pp247-258.
• [j] Bull, L. (2023) Evolving Multi-valued Regulatory Networks on Tuneable Fitness Landscapes. Complex Systems (in press)

Artificial Creativity: Surrogate Models, Open-Ended Search, 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 19:94-101
• [j] Preen, R. & Bull, L. (2017) On Design Mining: Coevolution and Surrogate Models. Artificial Life 23(2): 186-205
• [j] Preen, R., You, J., Bull, L. & Ieropoulos, I. (2019) Design Mining Microbial Fuel Cell Cascades. Soft Computing 23(13):4673-4683
• [j] Preen, R., Bull, L. & Adamatzky, A. (2019) Towards an Evolvable Cancer Treatment Simulator. BioSystems 182:1-7
• [j] Tsompanas, M-A., Bull, L., Adamatzky, A. & Balaz, I. (2020) Novelty Search Employed into the Development of Cancer Treatment Simulations. Informatics in Medicine Unlocked (in press)
• [j] Tsompanas, M-A., Bull, L., Adamatzky, A. & Balaz, I. (2021) In Silico Optimization of Cancer Therapies with Multiple types of Nanoparticles Applied at Different Times. Computer Methods and Programs in Biomedicine (in press)
• [j] Tsompanas, M-A., Bull, L., Adamatzky, A. & Balaz, I. (2021) Metameric Representations on Optimization of Nano Particle Cancer Treatment. Biocybernetics and Biomedical Engineering 41(2): 352-361
• [j] Tsompanas, M-A., Bull, L., Adamatzky, A. & Balaz, I. (2021) Evolutionary Algorithms Designing Nanoparticle Cancer Treatments with Multiple Particle Types. IEEE Computational Intelligence Magazine 16(4): 85-99

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.
• Bull, L. (2017) Haploid-Diploid Evolutionary Algorithms: the Baldwin Effect and Recombination Nature's Way. In Proceedings of the 2017 AISB Convention. AISB. (slides)
• [j] Bull, L. (2017) The Evolution of Sex through the Baldwin Effect. Artificial Life 23(4):481-492.
• [j] Bull, L. (2021) On the Emergence of Intersexual Selection: Arbitrary Trait Preference Improves Female-Male Coevolution. Artificial Life 27(1): 15-25.

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.
• [b] Bull, L., Toth, R., Stone, C., De Lacy Costello, B. & Adamatzky, A. (2017) Light-Sensitive Belousov-Zhabotinsky Computing through Simulated Evolution. In A. Adamatzky (ed) Advances in Unconventional Computing. Volume 2: Prototypes, Models and Algorithms. Springer, pp199-212.
• [b] Bull, L., Toth, R., Stone, C., De Lacy Costello, B. & Adamatzky, A. (2017) Chemical Computing through Simulated Evolution. In S. Stepney & A. Adamatzky (eds) Inspired by Nature. Springer, pp269-286.

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.
• [j] Whiting, J. G. H., Jones, J., Bull, L., Levin, M. & Adamatzky, A. (2016) Towards a Physarum learning chip. Scientific Reports 6: 19948.

Neuromorphic Systems: Memristors, Synapse, and Dendrites

• 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
• [j] Bull, L. (2022) Are Artificial Dendrites useful in Neuroevolution? Artificial Life 27(2): 75-79

top



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.



top

PhD Students

• Mihai Anca (current) - Learning Long Chains of Actions through Hierarchical Reinforcement Learning
• Sam Hunt (2021) - Music Content Analysis and Digitally Supported Musical Creativity
• Dom Brown (2019) - Gestural Languages for Gestural Musical Instruments
• 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 (see also here)

• BioMeld (EU: , 2022-2025)
Collaboration led by the University of Novi Sad, Serbia, on the optimisation of soft robotic catheters - with Andy Adamatzky (PI).


• EvoNano (EU: Anti Tsompanas, 2018-2022)
Collaboration led by the University of Novi Sad, Serbia, on the optimisation of nano particle cancer therapy - with Andy Adamatzky (PI).


• 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.


top