Abstract
A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems.
Similar content being viewed by others
Reference
Ahluwalia M, Bull L (1999) A genetic programming classifier system. In: Proceedings of the genetic and evolutionary computation conference, GECCO ’99. Morgan Kaufmann, San Francisco, pp 11–18
Angeline PJ (1997) An alternative to indexed memory for evolving programs with explicit state representations. In: Proceedings of the 2nd annual conference on genetic programming, Morgan Kaufmann, San Francisco, pp 423–430
Ashby WR (1952) Design for a Brain. Wiley, New York
Balan GC, Luke S (2004) A demonstration of neural programming applied to non-Markovian problems. In: Proceedings of the 6th annual conference on genetic and evolutionary computation, GECCO ’04. ACM, pp 422–433
Banzhaf W, Nordin P, Keller RE, Francone FD (1997) Genetic programming: an introduction: on the automatic evolution of computer programs and its applications. The Morgan Kaufmann Series in Artificial Intelligence. Morgan Kaufmann, San Francisco
Boyan J, Moore A (1995) Generalization in reinforcement learning: safely approximating the value function. In: Advances in neural information processing systems 7. NIPS. MIT Press, Denver, pp 369–376
Brave S (1996) Evolving recursive programs for tree search. In: Advances in genetic programming 2. MIT Press, Cambridge, chap 10, pp 203–220
Bull L (2002) On using constructivism in neural classifier systems. In: Merelo JJ, Adamidis P, Beyer HG (eds) Parallel problem solving from nature: PPSN VII. Lecture notes in computer science, vol 2439. Springer, Berlin, pp 558–567
Bull L (2009) On dynamical genetic programming: Simple boolean networks in learning classifier systems. International Journal of Parallel, Emergent and Distributed Systems 24:421–442
Bull L, Hurst J (2003) A neural learning classifier system with self-adaptive constructivism. In: The IEEE congress on evolutionary computation 2003, CEC ’03, vol 2. IEEE Press, New York, pp 991–997
Bull L, O’Hara T (2002) Accuracy-based neuro and neuro-fuzzy classifier systems. In: Proceedings of the genetic and evolutionary computation conference, GECCO ’02. Morgan Kaufmann Publishers Inc., San Francisco, pp 905–911
Bull L, Preen RJ (2009) On dynamical genetic programming: random boolean networks in learning classifier systems. In: Proceedings of the 12th European conference on genetic programming, EuroGP ’09. Springer, Berlin, pp 37–48
Bull L, Hurst J, Tomlinson A (2000) Self-adaptive mutation in classifier system controllers. In: Meyer JA, Berthoz A, Floreano D, Roitblat H, Wilson SW (eds) From animals to animats 6: proceedings of the sixth international conference on simulation of adaptive behavior. MIT Press, Cambridge, pp 460–468
Cao Y, Wang P, Tokuta A (2007) Gene regulatory network modeling: a data driven approach. In: Wang P, Ruan D, Kerre E (eds) Fuzzy logic, studies in fuzziness and soft computing, vol 215. Springer, Berlin, pp 247–281
Di J, Lala PK (2007) Cellular array-based delay-insensitive asynchronous circuits design and test for nanocomputing systems. Journal of Electronic Testing: Theory and Applications 23:175–192
Di Paulo E (2001) Rhythmic and non-rhythmic attractors in asynchronous random boolean networks. Biosystems 59:185–195
Elman JL (1990) Finding structure in time. Cognitive Science 14(2):179–211
Fogel LJ, Owens AJ, Walsh MJ (1965) Artificial intelligence through a simulation of evolution. In: Biophysics and cybernetic systems: proceedings of the 2nd cybernetic sciences symposium, Spartan Book Co., Washington, DC, pp 131–155
Gershenson C (2002) Classification of random boolean networks. In: Proceedings of the eighth international conference on artificial life. MIT Press, Cambridge, pp 1–8
Glass L, Kauffman SA (1973) The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology 39:1039–129
Harvey I, Bossomaier T (1997) Time out of joint: attractors in asynchronous random boolean networks. In: Proceedings of the fourth European artificial life conference, MIT Press, Cambridge, pp 67–75
Hirasawa K, Okubo M, Katagiri H, Hu J, Murata J (2001) Comparison between genetic network programming (GNP) and genetic programming (GP). In: Proceedings of the IEEE congress on evolutionary computation, 2001, vol 2. IEEE Press, New York, pp 1276–1282
Holland JH (1976) Adaptation. In: Rosen R, Snell FM (eds) Progress in theoretical biology, vol 4. Academic Press Inc., New York, pp 263–293
Howard GD, Bull L, Lanzi PL (2010) A spiking neural representation for XCSF. In: 2010 IEEE congress on evolutionary computation (CEC). IEEE Press, New York, pp 1–8
Ingerson TE, Buvel RL (1984) Structure in asynchronous cellular automata. Physica D: Nonlinear Phenomena 10:59–68
Kantschik W, Banzhaf W (2002) Linear-graph GP—a new GP structure. In: Proceedings of the 5th European conference on genetic programming, EuroGP ’02. Springer, London, pp 83–92
Kauffman SA (1993) The origins of order: self-organization and selection in evolution. Oxford University Press, Oxford
Kok T, Wang P (2006) A study of 3-gene regulation networks using NK-boolean network model and fuzzy logic networking. In: Kahraman C (ed) Fuzzy applications in industrial engineering, studies in fuzziness and soft computing, vol 201. Springer, Berlin, pp 119–151
Koza JR (1992) Genetic programming. MIT Press, Cambridge
Landau S, Picault S, Drogoul A (2001) ATNoSFERES: a model for evolutive agent behaviors. In: Proceedings of the AISB’01 symposium on adaptive agents and multi-agent systems
Landau S, Sigaud O, Schoenauer M (2005) ATNoSFERES revisited. In: Proceedings of the 2005 conference on genetic and evolutionary computation, GECCO ’05. ACM, New York, pp 1867–1874
Langdon WB (1998) Genetic programming and data structures: genetic programming + data structures = automatic programming!, genetic programming, vol 1. Kluwer, Boston
Lanzi PL (1999) An analysis of generalization in the XCS classifier system. Evolutionary Computation 7:125–149
Lanzi PL, Perrucci A (1999) Extending the representation of classifier conditions part II: from messy coding to S-expressions. In: Proceedings of the genetic and evolutionary computation conference, GECCO ’99. Morgan Kaufmann, San Francisco, pp 345–352
Lanzi PL, Wilson SW (2000) Toward optimal classifier system performance in non-Markov environments. Evolutionary Computation 8:393–418
Lanzi PL, Loiacono D, Wilson SW, Goldberg DE (2005) XCS with computed prediction in continuous multistep environments. In: The 2005 IEEE congress on evolutionary computation, vol 3. IEEE Press, New York, pp 2032–2039
Lemke N, Mombach JCM, Bodmann BEJ (2001) A numerical investigation of adaptation in populations of random boolean networks. Physica A: Statistical Mechanics and its Applications 301:589–600
Loiacono D, Lanzi PL (2008) Computed prediction in binary multistep problems. In: IEEE congress on evolutionary computation 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Press, New York, pp 3350–3357
Mellor D (2005) A first order logic classifier system. In: Proceedings of the 2005 conference on genetic and evolutionary computation, GECCO ’05. ACM, New York, pp 1819–1826
Mesot B, Teuscher C (2005) Deducing local rules for solving global tasks with random boolean networks. Physica D: Nonlinear Phenomena 211(1–2):88–106
Miller JF (1999) An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach. In: Proceedings of the genetic and evolutionary computation conference, GECCO ’99. Morgan Kaufmann, San Francisco, pp 1135–1142
Mitchell T (1997) Machine learning. McGraw Hill, New York
Moody JE, Darken C (1989) Fast learning in networks of locally-tuned processing units. Neural Computation 1:281–294
Mozer MC (1994) Neural net architectures for temporal sequence processing. In: Weigend AS, Gershenfeld NA (eds) Time series prediction: forecasting the future and understanding the past. Addison-Wesley, Reading, pp 243–264
Packard N (1988) Adaptation toward the edge of chaos. In: Kelso J, Mandell A, Shlesinger M (eds) Dynamic patterns in complex systems. World Scientific, Singapore, pp 293–301
Poli R, McPhee NF, Citi L, Crane E (2009) Memory with memory in genetic programming. J Artif Evol Appl 9:2:1–2:16
Preen RJ, Bull L (2009) Discrete dynamical genetic programming in XCS. In: Proceedings of the 11th annual conference on genetic and evolutionary computation, GECCO ’09. ACM, New York, pp 1299–1306
Preen RJ, Bull L (2013) Dynamical genetic programming in XCSF. Evol Comput (in press). doi:10.1162/EVCO_a_00080
Quick T, Nehaniv C, Dautenhahn K, Roberts G (2003) Evolving embedded genetic regulatory network-driven control systems. In: Proceedings of the seventh European artificial life conference, Springer, Heidelberg, pp 266–277
Ramirez Ruiz JA, Valenzuela-Rendón M, Terashima-Marín H (2008) QFCS: a fuzzy LCS in continuous multi-step environments with continuous vector actions. In: Rudolph G, Jansen T, Lucas SM, Poloni C, Beume N (eds) Parallel problem solving from nature: PPSN X, Springer, Berlin, pp 286–295
Reiter CA (2002) Fuzzy automata and life. Complexity 7(3):19–29
Schmidt M, Lipson H (2007) Comparison of tree and graph encodings as function of problem complexity. In: Proceedings of the 9th annual conference on genetic and evolutionary computation, GECCO ’07. ACM, New York, pp 1674–1679
Schwefel HP (1981) Numerical Optimization of Computer Models. John Wiley & Sons, Inc., New York, NY, USA
Shirakawa S, Ogino S, Nagao T (2007) Graph structured program evolution. In: Proceedings of the 9th annual conference on genetic and evolutionary computation, GECCO ’07. ACM, New York, pp 1686–1693
Sipper M (1997) Evolution of parallel cellular machines. Springer, Berlin
Sipper M, Ruppin E (1997) Co-evolving architectures for cellular machines. Physica D 99:428–441
Smith SF (1983) Flexible learning of problem solving heuristics through adaptive search. PhD Thesis, University of Pittsburgh
Spector L, Robinson A (2002) Genetic programming and autoconstructive evolution with the push programming language. Genet Progr Evol Mach 3:7–40
Su MC, Chou CH, Lai E, Lee J (2006) A new approach to fuzzy classifier systems and its application in self-generating neuro-fuzzy systems. Neurocomputing 69:586–614
Teller A (1994) The evolution of mental models. In: Advances in genetic programming. MIT Press, Cambridge, pp 199–219
Teller A, Veloso M (1996) Neural programming and an internal reinforcement policy. In: Koza JR (ed) Late breaking papers at the genetic programming 1996 conference, Stanford University, Stanford, pp 186–192
Tomlinson A (2001) CXCS: triggered linkage. Technical Report. UWELCSG01-003, University of the West of England. http://www.cems.uwe.ac.uk/lcsg/reports/uwelcsg01-003.ps.zip
Tran HT, Sanza C, Duthen Y, Nguyen TD (2007) XCSF with computed continuous action. In: Proceedings of the 9th annual conference on genetic and evolutionary computation, GECCO ’07. ACM, New York, pp 1861–1869
Turing AM (1948) Intelligent machinery. In: Evans CR, Robertson ADJ (eds) Cybernetics: key papers. University Park Press, Baltimore (1968)
Valenzuela-Rendón M (1991) The fuzzy classifier system: a classifier system for continuously varying variables. In: Proceedings of the fourth international conference on genetic algorithms. Morgan Kaufmann Publishers Inc., San Francisco, pp 346–353
Van den Broeck C, Kawai R (1990) Learning in feedforward boolean networks. Physical Review A 42(10):6210–6218
Von Neumann J (1966) The Theory of self-reproducing automata. University of Illinois, Urbana
Watkins CJCH (1989) Learning from delayed rewards. PhD Thesis, Cambridge University
Werner T, Akella V (1997) Asynchronous processor survey. Computer (USA) 30(11):67–76
Wilson SW (2000) Get real! XCS with continuous-valued inputs. In: Learning classifier systems, from foundations to applications, Springer, London, pp 209–222
Wilson SW (2001) Function approximation with a classifier system. In: Proceedings of the genetic and evolutionary computation conference, GECCO ’01. Morgan Kaufmann, San Francisco, pp 974–981
Wilson SW (2002) Classifiers that approximate functions. Natural Computing 1:211–234
Wilson SW (2004) Classifier systems for continuous payoff environments. In: Genetic and evolutionary computation GECCO 2004. Lecture notes in computer science, vol 3103. Springer, Berlin, pp 824–835
Wilson SW (2007) Three architectures for continuous action. In: Proceedings of the 2003–2005 international conference on learning classifier systems, IWLCS’03-05. Springer, Berlin, pp 239–257
Wuensche A (2004) Basins of attraction in network dynamics: a conceptual framework for biomolecular networks. In: Schlosser G, Wagner GP (eds) Modularity in development and evolution, Chicago University Press, Chicago, pp 288–311
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by D. Liu.
Rights and permissions
About this article
Cite this article
Preen, R.J., Bull, L. Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system. Soft Comput 18, 153–167 (2014). https://doi.org/10.1007/s00500-013-1044-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-013-1044-4