Genetics-Based Machine Learning

Abbass HA (2003) Speeding up backpropagation using multiobjective evolutionary algorithms. Neural Comput, 15(11):2705–2726CrossRefMATH

Ackley DH, Littman ML (1992) Interactions between learning and evolution. In: Langton C, Taylor C, Rasmussen S, Farmer J (eds) Artificial life II: Santa Fe institute studies in the sciences of Complexity, vol 10. Addison-Wesley, New York, pp 487–509

Aguilar-Ruiz J, Riquelme J, Toro M (2003) Evolutionary learning of hierarchical decision rules. IEEE Trans Syst Man Cybern B 33(2):324–331CrossRef

Ahluwalia M, Bull L (1999) A genetic programming-based classifier system. In: Banzhaf et al. (eds) GECCO-99: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, San Francisco, pp 11–18

Andersen HC, Tsoi AC (1993) A constructive algorithm for the training of a multi-layer perceptron based on the genetic algorithm. Complex Syst, 7(4):249–268MATH

Angeline PJ, Sauders GM, Pollack JB (1994) An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans Neural Netw 5:54–65CrossRef

Angelov P (2002) Evolving rule-based models: a tool for design of flexible adaptive systems. Studies in fuzziness and soft computing, vol 92. Springer, Heidelberg

Asuncion A, Newman DJ (2009) UCI machine learning repository. http://www.ics.uci.edu/∼mlearn/MLRepository.html

Bacardit J, Stout M, Hirst JD and Krasnogor N (2008) Data mining in proteomics with learning classifier systems. In: Bull L, Bernadó Mansilla E, Holmes J (eds) Learning classifier systems in data mining. Springer, Berlin, pp 17–46CrossRef

Bacardit J, Burke EK, Krasnogor N (2009a) Improving the scalability of rule-based evolutionary learning. Memetic Comput 1(1):55–57

Bacardit J, Stout M, Hirst JD, Valencia A, Smith RE, Krasnogor N (2009b) Automated alphabet reduction for protein datasets. BMC Bioinformatics. vol 10, 6

Bacardit J (2004) Pittsburgh genetic-based machine learning in the data mining era: representations, generalization, and run-time. PhD thesis, Universitat Ramon Llull, Barcelona, Spain

Bacardit J, Goldberg DE, Butz MV (2007) Improving the performance of a Pittsburgh learning classifier system using a default rule. In: Kovacs T, Llòra X, Takadama K, Lanzi PL, Stolzmann W, Wilson SW (eds) Learning Classifier Systems. International workshops, IWLCS 2003–2005, Revised selected papers, Lecture notes in computer science, vol 4399. Springer, Berlin, pp 291–307

Bacardit J, Krasnogor N (2008) Empirical evaluation of ensemble techniques for a Pittsburgh learning classifier system. In: Bacardit J, Bernadó-Mansilla E, Butz M, Kovacs T, Llorà X, Takadama K (eds) Learning Classifier Systems. 10th and 11th International Workshops (2006–2007), Lecture notes in computer science, vol 4998. Springer, Berlin, pp 255–268

Bagnall AJ, Zatuchna ZV (2005) On the classification of maze problems. In: Bull L, Kovacs T (eds) Applications of learning classifier systems. Springer, Berlin, pp 307–316

Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) (1999) GECCO-99: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, San Francisco, CA

Barry A (1996) Hierarchy formulation within classifiers system: a review. In: Goodman EG, Uskov VL, Punch WF (eds) Proceedings of the first international conference on evolutionary algorithms and their application EVCA'96. The Presidium of the Russian Academy of Sciences, Moscow, pp 195–211

Barry A (2000) XCS performance and population structure within multiple-step environments. PhD thesis, Queen’s University Belfast, Belfast

Beielstein T, Markon S (2002) Threshold selection, hypothesis tests and DOE methods. In: 2002 congress on evolutionary computation. IEEE Press, Washington, DC, pp 777–782

Belew RK, McInerney J, Schraudolph NN (1992) Evolving networks: using the genetic algorithm with connectionistic learning. In: Langton CG, Taylor C, Farmer JD, Rasmussen S (eds) Proceedings of the 2nd conference on artificial life. Addison-Wesley, New York, pp 51–548

Bernadó E, Llorà X, Garrell JM (2002) XCS and GALE: a comparative study of two learning classifier systems on data mining. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, Lecture notes in artificial intelligence, vol 2321. Springer, Berlin, pp 115–132CrossRef

Bernadó-Mansilla E, Garrell-Guiu JM (2003) Accuracy-based learning classifier systems: models, analysis and applications to classification tasks. Evolut Comput 11(3):209–238CrossRef

Bernadó-Mansilla E, Ho TK (2005) Domain of competence of XCS classifier system in complexity measurement space. IEEE Trans Evolut Comput 9(1):82–104CrossRef

Bonarini A (2000) An introduction to learning fuzzy classifier systems. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems: from foundations to applications, Lecture note in artificial intelligence, vol 1813. Springer, Berlin, pp 83–104

Bonelli P, Alexandre P (1991) An efficient classifier system and its experimental comparison with two representative learning methods on three medical domains. In: Booker LB, Belew RK (eds) Proceedings of the 14th international conference on genetic algorithms (ICGA'91). Morgan Kaufmann, San Francisco, CA, pp 288–295

Booker LB (1989) Triggered rule discovery in classifier systems. In: Schaffer JD (ed) Proceedings of the 3rd international conference on genetic algorithms (ICGA-89). Morgan Kaufmann, San Francisco, CA, pp 265–274

Booker LB (1991) Representing attribute-based concepts in a classifier system. In: Rawlins GJE (ed) Proceedings of the first workshop on foundations of genetic algorithms (FOGA91). Morgan Kaufmann, San Mateo, CA, pp 115–127

Booker LB (2005a) Adaptive value function approximations in classifier systems. In: GECCO '05: proceedings of the 2005 workshops on genetic and evolutionary computation. ACM, New York, pp 90–91

Booker LB (2005b) Approximating value functions in classifier systems. In: Bull L, Kovacs T (eds) Foundations of learning classifier systems (Studies in fuzziness and soft computing), Lecture notes in artificial intelligence, vol 183, Springer, Berlin, pp 45–61

Booker LB, Belew RK (eds) (1991) Proceedings of the 4th international conference on genetic algorithms (ICGA91). Morgan Kaufmann, San Francisco, CA

Bot MCJ, Langdon WB (2000) Application of genetic programming to induction of linear classification trees. In: Genetic programming: proceedings of the 3rd European conference (EuroGP 2000), Lecture notes in computer science, vol 1802, Springer, Berlin, pp 247–258

Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140MathSciNetMATH

Breiman L (1998) Arcing classifiers. Ann Stat 26(3):801–845MathSciNetCrossRefMATH

Brown G (2010) Ensemble learning. In: Sammut C, Webb G (eds) Encyclopedia of machine learning. Springer, Berlin

Brown G, Wyatt J, Harris R, Yao X (2005) Diversity creation methods: A survey and categorisation. J Inform Fusion 6(1):5–20CrossRef

Bull L (2009) On dynamical genetic programming: simple Boolean networks in learning classifier systems. IJPEDS 24(5):421–442

Bull L, Studley M, Bagnall T, Whittley I (2007) On the use of rule-sharing in learning classifier system ensembles. IEEE Trans Evolut Comput 11:496–502CrossRef

Bull L (2005) Two simple learning classifier systems. In: Bull L, Kovacs T (eds) Foundations of learning classifier systems (Studies in fuzziness and soft computing), Lecture notes in artificial intelligence, vol 183, Springer, Berlin, pp 63–90

Bull L, O'Hara T (2002) Accuracy-based neuro and neuro-fuzzy classifier systems. In: Langdon WB, Cantú-Paz E, Mathias K, Roy R, Davis R, Poli R, Balakrishnan K, Honavar V, Rudolph G, Wegener J, Bull L, Potter MA, Schultz AC, Miller JF, Burke E, Jonoska N (eds) GECCO 2002: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, San Francisco, CA, pp 905–911

Burke EK, Hyde MR, Kendall G, Ochoa G, Ozcan E, Woodward JR (2009) Exploring hyper-heuristic methodologies with genetic programming. In: Mumford C, Jain L (eds) Collaborative computational intelligence. Springer, Berlin

Burke EK, Kendall G (2005) Introduction. In: Burke EK, Kendall G (eds) Search methodologies: introductory tutorials in optimization and decision support techniques. Springer, Berlin, pp 5–18

Burke EK, Kendall G, Newall J, Hart E, Russ P, Schulenburg S (2003) Hyper-heuristics: an emerging direction in modern search technology. In: Glover F, Kochenberger G (eds) Handbook of meta-heuristics. Kluwer, Norwell, MA, pp 457–474

Butz MV, Kovacs T, Lanzi PL, Wilson SW (2004b) Toward a theory of generalization and learning in XCS. IEEE Trans Evolut Comput 8(1):8–46CrossRef

Butz MV (2002a) An algorithmic description of ACS2. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems: from foundations to applications, Lecture notes in artificial intelligence, vol 2321. Springer, Berlin, pp 211–229CrossRef

Butz MV (2002b) Anticipatory learning classifier systems. Kluwer, Norwell, MACrossRefMATH

Butz MV, Goldberg DV, Stolzmann W (2000a) Introducing a genetic generalization pressure to the anticipatory classifier system – part 1: theoretical approach. In: Whitley D, Goldberg D, Cantú-Paz E, Spector L, Parmee I, Beyer H-G (eds) Proceedings of genetic and evolutionary computation conference (GECCO 2000). Morgun Kaufmann, San Francisco, CA, pp 34–41

Butz MV, Goldberg DE, Stolzmann W (2000b) Introducing a genetic generalization pressure to the anticipatory classifier system – part 2: performance analysis. In: Whitley D, Goldberg D, Cantú-Paz E, Spector L, Parmee I, Beyer H-G (eds) Proceedings of genetic and evolutionary computation conference (GECCO 2000). Morgun Kaufmann, San Francisco, CA, pp 42–49

Butz MV, Wilson SW (2001) An algorithmic description of XCS. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, Lecture notes in artificial intelligence, vol 1996. Springer, Berlin, pp 253–272CrossRef

Butz MV (2005) Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system. In: Beyer HG et al. (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 2005). ACM, New York, pp 1835–1842CrossRef

Butz MV (2006) Rule-based evolutionary online learning systems: a principled approach to LCS analysis and design. Studies in fuzziness and soft computing. Springer, Berlin

Butz MV, Goldberg DE, Lanzi PL (2004a) Bounding learning time in XCS. In Genetic and evolutionary computation (GECCO 2004), Lecture notes in computer science, vol 3103. Springer, Berlin, pp 739–750

Butz MV, Goldberg DE, Lanzi PL (2005a) Computational complexity of the XCS classifier system. In: Bull L, Kovacs T (eds) Foundations of learning classifier systems, Studies in fuzziness and soft computing, Lecture notes in artificial intelligence, vol 183. Springer, Berlin, pp 91–126

Butz MV, Goldberg DE, Lanzi PL (2005b) Gradient descent methods in learning classifier systems: improving XCS performance in multistep problems. IEEE Trans Evolut Comput 9(5):452–473CrossRef

Butz MV, Goldberg DE, Lanzi PL, Sastry K (2007) Problem solution sustenance in XCS: Markov chain analysis of niche support distributions and the impact on computational complexity. GP and Evol Machines 8(1):5–37

Butz MV, Lanzi PL, Wilson SW (2006) Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure. In: Cattolico M (ed) Proceedings of the genetic and evolutionary computation conference (GECCO 2006). ACM, New York, pp 1457–1464CrossRef

Butz MV, Pelikan M (2006) Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions. In: Cattolico M et al. (eds) Genetic and evolutionary computation conference, GECCO 2006. ACM, New York, pp 1449–1456CrossRef

Butz MV, Pelikan M, Llorà X, Goldberg DE (2005) Extracted global structure makes local building block processing effective in XCS. In: Beyer HG, O'Reilly UM (eds) Proceedings of the genetic and evolutionary computation conference, GECCO 2005. ACM, New York, pp 655–662CrossRef

Butz MV, Pelikan M, Llorà X, Goldberg DE (2006) Automated global structure extraction for effective local building block processing in XCS. Evolut Comput 14(3):345–380CrossRef

Butz MV, Stalph P, Lanzi PL (2008) Self-adaptive mutation in XCSF. In GECCO '08: Proceedings of the 10th annual conference on genetic and evolutionary computation. ACM, New York, pp 1365–1372

Cantú-Paz E, Kamath C (2003) Inducing oblique decision trees with evolutionary algorithms. IEEE Trans Evolut Comput 7(1):54–68CrossRef

Cantú-Paz E (2002) Feature subset selection by estimation of distribution algorithms. In: GECCO '02: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, San Francisco, CA, pp 303–310

Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In: ICML '06: Proceedings of the 23rd international conference on machine learning. ACM, New York, pp 161–168

Casillas J, Carse B, Bull L (2007) Fuzzy-XCS: a Michigan genetic fuzzy system. IEEE Trans Fuzzy Syst 15:536–550CrossRef

Castilloa PA, Merelo JJ, Arenas MG, Romero G (2007) Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters. Inform Sciences, 177(14):2884–2905CrossRef

Chalmers D (1990) The evolution of learning: An experiment in genetic connectionism. In: Touretsky E (ed) Proceedings 1990 connectionist models summer school. Morgan Kaufmann, San Francisco, CA pp 81–90

Chandra A, Yao X (2006a) Ensemble learning using multi-objective evolutionary algorithms. J Math Model Algorithm 5(4):417–445MathSciNetCrossRefMATH

Chandra A, Yao X (2006b) Evolving hybrid ensembles of learning machines for better generalisation. Neurocomputing 69(7–9):686–700CrossRef

Cho S, Cha K (1996) Evolution of neural net training set through addition of virtual samples. In: Proceedings of the 1996 IEEE international conference on evolutionary computation. IEEE Press, Washington DC, pp 685–688

Cho S-B (1999) Pattern recognition with neural networks combined by genetic algorithm. Fuzzy Set Syst 103:339–347CrossRef

Cho S-B, Park C (2004) Speciated GA for optimal ensemble classifiers in DNA microarray classification. In: Congress on evolutionary computation (CEC 2004), vol 1. pp 590–597

Corcoran AL, Sen S (1994) Using real-valued genetic algorithms to evolve rule sets for classification. In: Proceedings of the IEEE conference on evolutionary computation. IEEE Press, Washington DC, pp 120–124

Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems. World Scientific, SingaporeMATH

Cribbs III HB, Smith RE (1996) Classifier system renaissance: new analogies, new directions. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic programming 1996: proceedings of the first annual conference. MIT Press, Cambridge, MA, Stanford University, CA, USA, 28–31 July 1996. pp 547–552

Dam HH, Abbass HA, Lokan C, Yao X (2008) Neural-based learning classifier systems. IEEE Trans Knowl Data Eng 20(1):26–39CrossRef

Dam HH, Abbass HA, Lokan C (2005) DXCS: an XCS system for distributed data mining. In: Beyer HG, O'Reilly UM (eds) Genetic and evolutionary computation conference, GECCO 2005. pp 1883–1890

Dasdan A, Oflazer K (1993) Genetic synthesis of unsupervised learning algorithms. Technical Report BU-CEIS-9306, Department of Computer Engineering and Information Science, Bilkent University, Ankara

De Jong KA, Spears WM (1991) Learning concept classification rules using genetic algorithms. In: Proceedings of the twelfth international conference on artificial intelligence IJCAI-91, vol 2. Morgan Kaufmann, pp 651–656

De Jong KA, Spears WM, Gordon DF (1993) Using genetic algorithms for concept learning. Mach Learn 3:161–188CrossRef

Dietterich TG (1998) Machine-learning research: four current directions. AI Mag 18(4):97–136

Divina F, Keijzer M, Marchiori E (2002) Non-universal suffrage selection operators favor population diversity in genetic algorithms. In: Benelearn 2002: proceedings of the 12th Belgian-Dutch conference on machine learning (Technical report UU-CS-2002-046). pp 23–30

Divina F, Keijzer M, Marchiori E (2003) A method for handling numerical attributes in GA-based inductive concept learners. In: Proceedings of the genetic and evolutionary computation conference (GECCO-2003). Springer, Berlin, pp 898–908

Divina F, Marchiori E (2002) Evolutionary concept learning. In: Langdon WB, Cantú-Paz E, Mathias K, Roy R, Davis D, Poli R, Balakrishnan K, Honavar V, Rudolph G, Wegener J, Bull L, Potter MA, Schultz AC, Miller JF, Burke E, Jonoska N (eds) GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, San Francisco, CA, New York, 9–13 July 2002. pp 343–350

Dixon PW, Corne D, Oates MJ (2002) A ruleset reduction algorithm for the XCS learning classifier system. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, 5th international workshop (IWLCS 2002), Lecture notes in computer science, vol 2661. Springer, Berlin, pp 20–29

Donnart J-Y (1998) Cognitive architecture and adaptive properties of an motivationally autonomous animat. PhD thesis, Université Pierre et Marie Curie. Paris, France

Donnart J-Y, Meyer J-A (1996a) Hierarchical-map Building and Self-positioning with MonaLysa. Adapt Behav 5(1):29–74CrossRef

Donnart J-Y, Meyer J-A (1996b) Learning reactive and planning rules in a motivationally autonomous animat. IEEE Trans Syst Man Cybern B Cybern 26(3):381–395CrossRef

Dorigo M, Colombetti M (1998) Robot shaping: an experiment in behavior engineering. MIT Press/Bradford Books, Cambridge, MA

Drugowitsch J, Barry A (2005) XCS with eligibility traces. In: Beyer H-G, O'Reilly U-M (eds) Genetic and evolutionary computation conference, GECCO 2005. ACM, New York, pp 1851–1858CrossRef

Drugowitsch J (2008) Design and analysis of learning classifier systems: a probabilistic approach. Springer, BerlinMATH

Drugowitsch J, Barry A (2007) A formal framework and extensions for function approximation in learning classifier systems. Mach Learn 70(1):45–88CrossRef

Edakunni NE, Kovacs T, Brown G, Marshall JAR, Chandra A (2009) Modelling UCS as a mixture of experts. In: Proceedings of the 2009 Genetic and Evolutionary Computation Conference (GECCO'09). ACM, pp 1187–1994

Floreano D, Dürr P, Mattiussi C (2008) Neuroevolution: from architectures to learning. Evol Intell 1(1):47–62CrossRef

Folino G, Pizzuti C, Spezzano G (2003) Ensemble techniques for parallel genetic programming based classifiers. In: Proceedings of the sixth European conference on genetic programming (EuroGP'03), Lecture notes in computer science, vol 2610. Springer, Berlin, pp 59–69

Freitas AA (2002a) Data mining and knowledge discovery with evolutionary algorithms. Spinger, BerlinMATH

Freitas AA (2002b) A survey of evolutionary algorithms for data mining and knowledge discovery. In: Ghosh A, Tsutsui S (eds) Advances in evolutionary computation. Springer, Berlin, pp 819–845

Freund Y, Schapire R (1996) Experiments with a new boosting algorithm. In: Proceedings of the international conference on machine learning (ICML'96), Bari, Italy, pp 148–156

Freund Y, Schapire R (1999) A short introduction to boosting. J Jpn Soc Artif Intell 14(5):771–780

Fürnkranz J (1998) Integrative windowing. J Artif Intell Res 8:129–164MATH

Gagné C, Sebag M, Schoenauer M, Tomassini M (2007) Ensemble learning for free with evolutionary algorithms? In: GECCO '07: Proceedings of the 9th annual conference on genetic and evolutionary computation. ACM, New York, pp 1782–1789

Gathercole C, Ross P (1997) Tackling the Boolean even n parity problem with genetic programming and limited-error fitness. In: Koza JR, Deb K, Dorigo M, Fogel DB, Garzon M, Iba H, Riolo RL (eds) Genetic programming 1997: proceedings second annual conference. Morgan Kaufmann, San Francisco, CA, pp 119–127

Gérard P, Sigaud O (2003) Designing efficient exploration with MACS: Modules and function approximation. In: Cantú-Paz E, Foster JA, Deb K, Davis D, Roy R, O'Reilly U-M, Beyer H-G, Standish R, Kendall G, Wilson S, Harman M, Wegener J, Dasgupta D, Potter MA, Schultz AC, Dowsland K, Jonoska N, Miller J (eds) Genetic and evolutionary computation – GECCO-2003, Lecture notes in computer science, vol 2724. Springer, Berlin, pp 1882–1893CrossRef

Gerard P, Stolzmann W, Sigaud O (2002) YACS, a new learning classifier system using anticipation. J Soft Comput 6(3–4):216–228MATH

Geyer-Schulz A (1997) Fuzzy rule-based expert systems and genetic machine learning. Physica, Heidelberg

Giordana A, Neri F (1995) Search-intensive concept induction. Evolut Comput 3:375–416CrossRef

Giordana A, Saitta L (1994) Learning disjunctive concepts by means of genetic algorithms. In: Proceedings of the international conference on machine learning, Brunswick, NJ, pp 96–104

Giraldez R, Aguilar-Ruiz J, Riquelme J (2003) Natural coding: a more efficient representation for evolutionary learning. In: Proceedings of the genetic and evolutionary computation conference (GECCO-2003). Springer, Berlin, pp 979–990

Giraud-Carrier C, Keller J (2002) Meta-learning. In: Meij J (ed) Dealing with the data flood. STT/Beweton, Hague, The Netherlands

Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MAMATH

Goldberg DE, Horn J, Deb K (1992) What makes a problem hard for a classifier system? In: Collected abstracts for the first international workshop on learning classifier system (IWLCS-92). (Also technical report 92007 Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign). Available from ENCORE (ftp://ftp.krl.caltech.edu/pub/EC/Welcome.html) in the section on Classifier Systems

Greene DP, Smith SF (1993) Competition-based induction of decision models from examples. Mach Learn 13:229–257CrossRef

Greene DP, Smith SF (1994) Using coverage as a model building constraint in learning classifier systems. Evolut Comput 2(1):67–91CrossRef

Greene DP, Smith SF (1987) A genetic system for learning models of consumer choice. In: Proceedings of the second international conference on genetic algorithms and their applications. Morgan Kaufmann, San Francisco, CA, pp 217–223

Greenyer A (2000) The use of a learning classifier system JXCS. In: van der Putten P, van Someren M (eds) CoIL challenge 2000: the insurance company case. Leiden Institute of Advanced Computer Science, June 2000. Technical report 2000–09

Gruau F (1995) Automatic definition of modular neural networks. Adapt Behav 3(2):151–183CrossRef

Hanebeck D, Schmidt K (1996) Genetic optimization of fuzzy networks. Fuzzy set syst 79:59–68MathSciNetCrossRef

Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12(10): 993–1001CrossRef

Hart WE, Krasnogor N, Smith JE (eds) (2004) Special issue on memetic algorithms, evolutionary computation, vol 12, 3

Hart WE, Krasnogor N, Smith JE (eds) (2005) Recent advances in memetic algorithms, Studies in fuzziness and soft computing, vol 166. Springer, Berlin

He L, Wang KJ, Jin HZ, Li GB, Gao XZ (1999) The combination and prospects of neural networks, fuzzy logic and genetic algorithms. In: IEEE midnight-sun workshop on soft computing methods in industrial applications. IEEE, Washington, DC, pp 52–57

Heitkötter J, Beasley D (2001) The hitch-hiker's guide to evolutionary computation (FAQ for comp.ai.genetic). Accessed 28/2/09. http://www.aip.de/∼ast/EvolCompFAQ/

Hekanaho J (1995) Symbiosis in multimodal concept learning. In: Proceedings of the 1995 international conference on machine learning (ICML'95). Morgan Kaufmann, San Francisco, pp 278–285

Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Evolut Intell 1(1):27–46MathSciNetCrossRef

Holland JH (1976) Adaptation. In: Rosen R, Snell FM (eds) Progress in theoretical biology. Plenum, New York

Holland JH (1986) Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In: Mitchell T, Michalski R, Carbonell J (eds) Machine learning, an artificial intelligence approach. vol. II, chap. 20. Morgan Kaufmann, San Francisco, CA, pp 593–623

Holland JH, Booker LB, Colombetti M, Dorigo M, Goldberg DE, Forrest S, Riolo RL, Smith RE, Lanzi PL, Stolzmann W, Wilson SW (2000) What is a learning classifier system? In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems: from foundations to application, Lecture notes in artificial intelligence, vol 1813. Springer, Berlin, pp 3–32CrossRef

Holland JH, Holyoak KJ, Nisbett RE, Thagard PR (1986) Induction: processes of inference, learning, and discovery. MIT Press, Cambridge, MA

Holland JH, Reitman JS (1978) Cognitive systems based on adaptive algorithms. In: Waterman DA, Hayes-Roth F (eds) Pattern-directed Inference Systems. Academic Press, New York. Reprinted in: Evolutionary Computation. The Fossil Record. Fogel DB (ed) (1998) IEEE Press, Washington DC. ISBN: 0-7803-3481-7

Homaifar A, Mccormick E (1995) Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Trans Fuzzy Syst, 3(2):129–139CrossRef

Howard D, Bull L (2008) On the effects of node duplication and connection-orientated constructivism in neural XCSF. In: Keijzer M et al. (eds) GECCO-2008: proceedings of the genetic and evolutionary computation conference. ACM, New York, pp 1977–1984CrossRef

Howard D, Bull L, Lanzi PL (2008) Self-adaptive constructivism in neural XCS and XCSF. In: Keijzer M et al. (eds) GECCO-2008: proceedings of the genetic and evolutionary computation conference. ACM, New York, pp 1389–1396CrossRef

Hu Y-J (1998) A genetic programming approach to constructive induction. In: Genetic programming 1998: proceedings of the 3rd annual conference. Morgan Kaufmann, San Francisco, CA, pp 146–151

Hurst J, Bull L (2003) Self-adaptation in classifier system controllers. Artifi Life Robot 5(2):109–119CrossRef

Hurst J, Bull L (2004) A self-adaptive neural learning classifier system with constructivism for mobile robot control. In: Yao X et al. (eds) Parallel problem solving from nature (PPSN VIII), Lecture notes in computer science, vol 3242. Springer, Berlin, pp 942–951CrossRef

Husbands P, Harvey I, Cliff D, Miller G (1994) The use of genetic algorithms for the development of sensorimotor control systems. In: Gaussier P, Nicoud J-D (eds) From perception to action. IEEE Press, Washington DC, pp 110–121

Iba H (1999) Bagging, boosting and bloating in genetic programming. In: Proceedings of the genetic and evolutionary computation conference (GECCO'99). Morgan Kaufmann, San Francisco, CA, pp 1053–1060

IEEE (2000) Proceedings of the 2000 congress on evolutionary computation (CEC'00). IEEE Press, Washington DC

Ishibuchi H, Nakashima T (2000) Multi-objective pattern and feature selection by a genetic algorithm. In: Proceedings of the 2000 genetic and evolutionary computation conference (GECCO'2000). Morgan Kaufmann, San Francisco, CA, pp 1069–1076

Islam MM, Yao X, Murase K (2003) A constructive algorithm for training cooperative neural network ensembles. IEEE Trans Neural Networ 14:820–834CrossRef

Jain A, Zongker D (1997) Feature selection: evaluation, application and small sample performance. IEEE Trans. Pattern Anal Mach Intell 19(2):153–158CrossRef

Janikow CZ (1991) Inductive learning of decision rules in attribute-based examples: a knowledge-intensive genetic algorithm approach. PhD thesis, University of North Carolina

Janikow CZ (1993) A knowledge-intensive genetic algorithm for supervised learning. Mach Learn 13:189–228CrossRef

Jin Y, Sendhoff B (2004) Reducing fitness evaluations using clustering techniques and neural network ensembles. In: Genetic and evolutionary computation conference (GECCO–2004), Lecture notes in computer science, vol 3102. Springer, Berlin, pp 688–699

John G, Kohavi R, Phleger K (1994) Irrelevant features and the feature subset problem. In: Proceedings of the 11th international conference on machine learning. Morgan Kaufmann, San Francisco, CA, pp 121–129

Juan Liu J, Tin-Yau Kwok J (2000) An extended genetic rule induction algorithm. In: Proceedings of the 2000 congress on evolutionary computation (CEC'00). IEEE Press, Washington DC, pp 458–463

Kelly JD Jr, Davis L (1991) Hybridizing the genetic algorithm and the k nearest neighbors classification algorithm. In: Booker LB, Belew RK (eds) Proceedings of the 4th international conference on genetic algorithms (ICGA91). Morgan Kaufmann, San Francisco, CA, pp 377–383

Karr C (1991) Genetic algorithms for fuzzy controllers. AI Expert 6(2):26–33

Kasabov N (2007) Evolving connectionist systems: the knowledge engineering approach. Springer, BerlinMATH

Keijzer M, Babovic V (2000) Genetic programming, ensemble methods, and the bias/variance/tradeoff – introductory investigation. In: Proceedings of the European conference on genetic programming (EuroGP'00), Lecture notes in computer science, vol 1802. Springer, Berlin, pp 76–90

Kitano H (1990) Designing neural networks by genetic algorithms using graph generation system. J Complex Syst 4:461–476MATH

Kolman E, Margaliot M (2009) Knowledge-based neurocomputing: a fuzzy logic approach, Studies in fuzziness and soft computing, vol 234. Springer, BerlinCrossRef

Kovacs T (1996) Evolving optimal populations with XCS classifier systems. Master's thesis, University of Birmingham, Birmingham, UK

Kovacs T (1997) XCS classifier system reliably evolves accurate, complete, and minimal representations for Boolean functions. In: Chawdhry PK, Roy R, Pant RK (eds) Soft computing in engineering design and manufacturing. Springer, London, pp 59–68 ftp://ftp.cs.bham.ac.uk/pub/authors/T.Kovacs/index.html

Kovacs T (2000) Strength or accuracy? Fitness calculation in learning classifier systems. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems: from foundations to applications, Lecture notes in artificial intelligence, vol 1813. Springer, Berlin, pp 143–160CrossRef

Kovacs T (2004) Strength or accuracy: credit assignment in learning classifier systems. Springer, BerlinCrossRefMATH

Kovacs T (2009) A learning classifier systems bibliography. Department of Computer Science, University of Bristol. http://www.cs.bris.ac.uk/∼kovacs/lcs/search.html

Kovacs T, Kerber M (2001) What makes a problem hard for XCS? In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, Lecture notes in artificial intelligence, vol 1996. Springer, Berlin, pp 80–99CrossRef

Kovacs T, Kerber M (2004) High classification accuracy does not imply effective genetic search. In: Deb K et al. (eds) Proceedings of the 2004 genetic and evolutionary computation conference (GECCO), Lecture notes in computer science, vol 3102. Springer, Berlin, pp 785–796

Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MAMATH

Koza JR (1994) Genetic Programming II. MIT Press

Krasnogor N (2002) Studies on the theory and design space of memetic algorithms. PhD thesis, University of the West of England

Krasnogor N, Smith JE (2005) A tutorial for competent memetic algorithms: model, taxonomy and design issues. IEEE Trans Evolut Comput 9(5):474–488CrossRef

Krasnogor N (2004) Self-generating metaheuristics in bioinformatics: the protein structure comparison case. GP and Evol Machines 5(2):181–201

Krasnogor N, Gustafson S (2004) A study on the use of self-generation in memetic algorithms. Natural Comput 3(1):53–76MathSciNetCrossRefMATH

Krawiec K (2002) Genetic programming-based construction of features for machine learning and knowledge discovery tasks. GP and Evol Machines 3(4):329–343MATH

Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation and active learning. NIPS 7:231–238

Kudo M, Skalansky J (2000) Comparison of algorithms that select features for pattern classifiers. Pattern Recogn 33:25–41CrossRef

Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, HobokenCrossRefMATH

Kushchu I (2002) An evaluation of evolutionary generalization in genetic programming. Artif Intell Rev 18(1):3–14CrossRefMATH

Lam L, Suen CY (1995) Optimal combination of pattern classifiers. Pattern Recogn Lett 16:945–954 See Kuncheva (2004a) p.167CrossRef

Landau S, Sigaud O, Schoenauer M (2005) ATNoSFERES revisited. In: Proceedings of the genetic and evolutionary computation conference GECCO-2005. ACM, New York, pp 1867–1874

Langdon W, Gustafson S, Koza J (2009) The genetic programming bibliography. http://www.cs.bham.ac.uk/∼wbl/biblio/

Lanzi PL (1999a) Extending the representation of classifier conditions, part I: from binary to messy coding. In: Banzhaf W et al. (eds) GECCO-99: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, San Francisco, CA, pp 337–344

Lanzi PL (1999b) Extending the representation of classifier conditions part II: from messy coding to S-expressions. In: Banzhaf W et al. (eds) GECCO-99: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, San Francisco, CA, pp 345–352

Lanzi PL (2002a) Learning classifier systems from a reinforcement learning perspective. J Soft Comput 6(3–4):162–170CrossRefMATH

Lanzi PL (2001) Mining interesting knowledge from data with the XCS classifier system. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt H-M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO-2001). Morgan Kaufmann, San Francisco, CA, pp 958–965

Lanzi PL (2008) Learning classifier systems: then and now. Evolut Intell 1(1):63–82CrossRef

Lanzi PL, Loiacono D, Wilson SW, Goldberg DE (2006a) Classifier prediction based on tile coding. In: Genetic and evolutionary computation – GECCO-2006. ACM, New York, pp 1497–1504

Lanzi PL, Loiacono D, Wilson SW, Goldberg DE (2006b) Prediction update algorithms for XCSF: RLS, Kalman filter and gain adaptation. In: Genetic and Evolutionary Computation – GECCO-2006. ACM, New York, pp 1505–1512

Lanzi PL, Loiacono D, Zanini M (2008) Evolving classifiers ensembles with heterogeneous predictors. In: Bacardit J, Bernadó-Mansilla E, Butz M, Kovacs T, Llorà X, Takadama K (eds) Learning classifier systems. 10th and 11th international workshops (2006–2007), Lecture notes in computer science, vol 4998. Springer, Berlin, pp 218–234

Lanzi PL, Riolo RL (2000) A roadmap to the last decade of learning classifier system research (from 1989 to 1999). In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems: from foundations to applications, Lecture notes in artificial intelligence, vol 1813. Springer, Berlin, pp 33–62CrossRef

Lanzi PL, Stolzmann W, Wilson SW (eds) (2000) Learning classifier systems: from foundations to applications, Lecture notes in artificial intelligence, vol 1813. Springer, Berlin

Lanzi PL, Stolzmann W, Wilson SW (eds) (2001) Advances in learning classifier systems, Lecture notes in artificial intelligence, vol 1996. Springer, Berlin

Lanzi PL, Stolzmann W, Wilson SW (eds) (2002) Advances in learning classifier systems, Lecture notes in artificial intelligence, vol 2321. Springer, BerlinMATH

Lanzi PL, Butz MV, Goldberg DE (2007) Empirical analysis of generalization and learning in XCS with gradient descent. In: Lipson H (ed) Proceedings of the Genetic and evolutionary computation conference, GECCO 2007, vol 2. ACM, New York, pp 1814–1821CrossRef

Lanzi PL, Loiacono D (2006) Standard and averaging reinforcement learning in XCS. In: Cattolico M (ed) Proceedings of the 8th annual conference on genetic and evolutionary computation, GECCO 2006. ACM, New York, pp 1480–1496

Lanzi PL, Loiacono D (2007) Classifier systems that compute action mappings. In: Lipson H (ed) Proceedings of the Genetic and evolutionary computation conference, GECCO 2007. ACM, New York, pp 1822–1829CrossRef

Lanzi PL, Wilson SW (2006) Using convex hulls to represent classifier conditions. In: Cattolico M (ed) Proceedings of the genetic and evolutionary computation conference (GECCO 2006). ACM, New York, pp 1481–1488CrossRef

Liangjie Z, Yanda L (1996) A new global optimizing algorithm for fuzzy neural networks. Int J Elect 80(3):393–403CrossRef

Linkens DA, Nyongesa HO (1996) Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications. IEE Proc Contr Theo Appl 143(4):367–386CrossRefMATH

Liu Y, Yao X (1999) Ensemble learning via negative correlation. Neural Networ 12:1399–1404CrossRef

Liu Y, Yao X, Higuchi T (2000) Evolutionary ensembles with negative correlation learning. IEEE Trans Evolut Comput 4(4):380–387CrossRef

Llorà X (2002) Genetic based machine learning using fine-grained parallelism for data mining. PhD thesis, Enginyeria i Arquitectura La Salle. Ramon Llull University

Llorà X, Garrell JM (2001) Knowledge-independent data mining with fine-grained parallel evolutionary algorithms. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt H-M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO'2001). Morgan Kaufmann, San Francisco, CA, pp 461–468

Llorà X, Sastry K, Goldberg DE (2005a) Binary rule encoding schemes: a study using the compact classifier system. In: Rothlauf F (ed) Proceedings of the 2005 conference on genetic and evolutionary computation GECCO '05. ACM Press, New York, pp 88–89CrossRef

Llorà X, Sastry K, Goldberg DE (2005b) The compact classifier system: scalability analysis and first results. In: Rothlauf F (ed) Proceedings of the IEEE congress on evolutionary computation, CEC 2005. IEEE, Press, Washington, DC, pp 596–603CrossRef

Llorà X, Wilson SW (2004) Mixed decision trees: minimizing knowledge representation bias in LCS. In: Kalyanmoy Deb et al. (eds) Proceedings of the genetic and evolutionary computation conference (GECCO-2004), Lecture notes in computer science, Springer, Berlin, pp 797–809

Loiacono D, Marelli A, Lanzi PL (2007) Support vector regression for classifier prediction. In: GECCO '07: Proceedings of the 9th annual conference on genetic and evolutionary computation. ACM, Berlin, pp 1806–1813

Marmelstein RE, Lamont GB (1998) Pattern classification using a hybrid genetic algorithm – decision tree approach. In: Genetic programming 1998: proceedings of the 3rd annual conference (GP'98). Morgan Kaufmann, San Francisco, CA, pp 223–231

Marshall JAR, Kovacs T (2006) A representational ecology for learning classifier systems. In: Keijzer M et al. (ed) Proceedings of the 2006 genetic and evolutionary computation conference (GECCO 2006). ACM, New York, pp 1529–1536

Martin-Bautista MJ, Vila M-A (1999) A survey of genetic feature selection in mining issues. In: Proceedings of the congress on evolutionary computation (CEC'99). IEEE Press, Washington, DC, pp 1314–1321

Meir R, Rätsch G (2003) An introduction to boosting and leveraging. In: Advanced lectures on machine learning. Springer, Berlin, pp 118–183

Mellor D (2005a) A first order logic classifier system. In: Rothlauf F (ed), GECCO '05: Proceedings of the 2005 conference on genetic and evolutionary computation. ACM, New York, pp 1819–1826CrossRef

Mellor D (2005b) Policy transfer with a relational learning classifier system. In: GECCO Workshops 2005. ACM, New York, pp 82–84

Mellor D (2008) A learning classifier system approach to relational reinforcement learning. In: Bacardit J, Bernadó-Mansilla E, Butz M, Kovacs T, Llorà X, Takadama K (eds) Learning classifier systems. 10th and 11th international workshops (2006–2007), Lecture notes in computer science, vol 4998. Springer, New York, pp 169–188

Michalski RS, Mozetic I, Hong J, Lavrac N (1986) The AQ15 inductive learning system: an overview and experiments. Technical Report UIUCDCS-R-86-1260, University of Illinois

Miller GF, Todd PM, Hegde SU (1989) Designing neural networks using genetic algorithms. In: Schaffer JD (ed) Proceedings of the 3rd international conference genetic algorithms and their applications, Morgan Kaufmann, San Francisco, CA, pp 379–384

Mitra S, Hayashi Y (2000) Neurofuzzy rule generation: survey in soft computing framework. IEEE Trans Neural Networ 11(3):748–768CrossRef

Morimoto T, Suzuki J, Hashimoto Y (1997) Optimization of a fuzzy controller for fruit storage using neural networks and genetic algorithms. Eng Appl Artif Intell 10(5):453–461CrossRef

Nolfi S, Miglino O, Parisi D (1994) Phenotypic plasticity in evolving neural networks. In: Gaussier P, Nicoud J-D (eds) From perception to action. IEEE Press, Washington, DC, pp 146–157

O'Hara T, Bull L (2005) A memetic accuracy-based neural learning classifier system. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2005). IEEE Press, Washington, DC, pp 2040–2045

Ong Y-S, Krasnogor N, Ishibuchi H (eds) (2007) Special issue on memetic algorithms, IEEE Transactions on Systems, Man and Cybernetics - Part B

Ong Y-S, Lim M-H, Neri F, Ishibuchi H (2009) Emerging trends in soft computing - memetic algorithms, Special Issue of Soft Computing. vol 13, 8–9

Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: A comparative study. IEEE Trans Syst Man Cybern B 36(1):141–152CrossRef

Opitz D, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169–198MATH

Opitz DW, Shavlik JW (1996) Generating accurate and diverse members of a neural-network ensemble. Advances in neural information processing systems, vol 8. Morgan Kaufmann, pp 535–541

Orriols-Puig A, Bernadó-Mansilla E (2006) Bounding XCS's parameters for unbalanced datasets. In: Keijzer M et al. (eds) Proceedings of the 2006 genetic and evolutionary computation conference (GECCO 2006). ACM, New York, pp 1561–1568

Orriols-Puig A, Casillas J, Bernadò-Mansilla E (2007a) Fuzzy-UCS: preliminary results. In: Lipson H (ed) Proceedings of the genetic and evolutionary computation conference, GECCO 2007. ACM, New York, pp 2871–2874CrossRef

Orriols-Puig A, Goldberg DE, Sastry K, Bernadó-Mansilla E (2007b) Modeling XCS in class imbalances: population size and parameter settings. In: Lipson H et al. (eds) Genetic and evolutionary computation conference, GECCO 2007. ACM, New York, pp 1838–1845CrossRef

Orriols-Puig A, Goldberg DE, Sastry K, Bernadó-Mansilla E (2007c) Modeling XCS in class imbalances: population size and parameter settings. In: Lipson H (eds) Proceedings of the genetic and evolutionary computation conference, GECCO 2007. ACM, New York, pp 1838–1845CrossRef

Orriols-Puig A, Sastry K, Lanzi PL, Goldberg DE, Bernadò-Mansilla E (2007d) Modeling selection pressure in XCS for proportionate and tournament selection. In: Lipson H (ed) Proceedings of the genetic and evolutionary computation conference, GECCO 2007. ACM, New York, pp 1846–1853CrossRef

Orriols-Puig A, Bernadó-Mansilla E (2008) Revisiting UCS: description, fitness sharing, and comparison with XCS. In: Bacardit J, Bernadó-Mansilla E, Butz M, Kovacs T, Llorà X, Takadama K (eds) Learning classifier systems. 10th and 11th international workshops (2006–2007), Lecture notes in computer science, vol 4998. Springer, Berlin, pp 96–111

Orriols-Puig A, Casillas J, Bernadó-Mansilla E (2008a) Evolving fuzzy rules with UCS: preliminary results. In: Bacardit J, Bernadó-Mansilla E, Butz M, Kovacs T, Llorà X, Takadama K (eds) Learning classifier systems. 10th and 11th international workshops (2006–2007), Lecture notes in computer science, vol 4998. Springer, Berlin, pp 57–76

Orriols-Puig A, Casillas J, Bernadó-Mansilla E (2008b) Genetic-based machine learning systems are competitive for pattern recognition. Evolut Intell 1(3):209–232CrossRef

Pal S, Bhandari D (1994) Genetic algorithms with fuzzy fitness function for object extraction using cellular networks. Fuzzy Set Syst 65(2–3):129–139CrossRef

Pappa GL, Freitas AA (2010) Automating the design of data mining algorithms. An evolutionary computation approach. Natural computing series. Springer

Paris G, Robilliard D, Fonlupt C (2001) Applying boosting techniques to genetic programming. In: Artificial evolution 2001, Lecture notes in computer science, vol 2310. Springer, Berlin, pp 267–278

Pereira FB, Costa E (2001) Understanding the role of learning in the evolution of busy beaver: a comparison between the Baldwin effect and Lamarckian strategy. In: Proceedings of the genetic and evolutionary computation conference (GECCO–2001). Morgan Kaufmann, San Francisco, pp 884–891

Perneel C, Themlin J-M (1995) Optimization of fuzzy expert systems using genetic algorithms and neural networks. IEEE Trans Fuzzy Syst 3(3):301–312CrossRef

Pham DT, Karaboga D (1991) Optimum design of fuzzy logic controllers using genetic algorithms. J Syst Eng 1:114–118

Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming, freely available at http://www.gp-field-guide.org.uk.lulu.com

Punch WF, Goodman ED, Pei M, Chia-Shun L, Hovland P, Enbody R (1993) Further research on feature selection and classification using genetic algorithms. In: Forrest S (ed) Proceedings of the 5th international conference on genetic algorithms (ICGA93). Morgan Kaufmann, San Francisco, CA, pp 557–564

Radi A, Poli R (2003) Discovering efficient learning rules for feedforward neural networks using genetic programming. In: Abraham A, Jain L, Kacprzyk J (eds) Recent advances in intelligent paradigms and applications. Springer, Berlin, pp 133–159

Raymer ML, Punch WF, Goodman ED, Kuhn LA, Jain AK (2000) Dimensionality reduction using genetic algorithms. IEEE Trans Evolut Comput 4(2):164–171CrossRef

Reeves CR, Rowe JE (2002) Genetic algorithms – principles and perspectives. A guide to GA theory. Kluwer, Norwell

Riolo RL (1987) Bucket brigade performance: I. long sequences of classifiers. In: Grefenstette JJ (eds) Proceedings of the 2nd international conference on genetic algorithms (ICGA'87), Lawrence Erlbaum Associates, Cambridge, MA, pp 184–195

Rivest RL (1987) Learning decision lists. Mach Learn 2(3):229–246

Romaniuk S (1994) Towards minimal network architectures with evolutionary growth networks. In: Proceedings of the 1993 international joint conference on neural networks, vol 3. IEEE Press, Washington, DC, pp 1710–1713

Rouwhorst SE, Engelbrecht AP (2000) Searching the forest: using decision trees as building blocks for evolutionary search in classification databases. In: Proceedings of the 2000 congress on evolutionary computation (CEC00). IEEE Press, Washington, DC, pp 633–638

Rozenberg G, Bäck T, Kok J (eds) (2012) Handbook of natural computing. Springer, BerlinMATH

Ruta D, Gabrys B (2001) Application of the evolutionary algorithms for classifier selection in multiple classifier systems with majority voting. In: Kittler J, Roli F (eds) Proceedings of the 2nd international workshop on multiple classifier systems, Lecture notes in computer science, vol 2096. Springer, Berlin, pp 399–408. See Kuncheva (2004a) p.321CrossRef

Sánchez L, Couso I (2007) Advocating the use of imprecisely observed data in genetic fuzzy systems. IEEE Trans Fuzzy Syst 15(4):551–562CrossRef

Sasaki T, Tokoro M (1997) Adaptation toward changing environments: why Darwinian in nature? In: Husbands P, Harvey I (eds) Proceedings of the 4th European conference on artificial life. MIT Press, Cambridge, MA, pp 145–153

Saxon S, Barry A (2000) XCS and the Monk's problems. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems: from foundations to applications, Lecture notes in artificial intelligence, vol 1813. Springer, Berlin, pp 223–242CrossRef

Schaffer C (1994) A conservation law for generalization performance. In: Hirsh H, Cohen WW (eds) Machine learning: proceedings of the eleventh international conference. Morgan Kaufmann, San Francisco, CA, pp 259–265

Schaffer JD (ed) (1989) Proceedings of the 3rd international conference on genetic algorithms (ICGA-89), George Mason University, June 1989. Morgan Kaufmann, San Francisco, CA

Schmidhuber J (1987) Evolutionary principles in self-referential learning. (On learning how to learn: The meta-meta-… hook.). PhD thesis, Technische Universität München, Germany

Schuurmans D, Schaeffer J (1989) Representational difficulties with classifier systems. In: Schaffer JD (ed) Proceedings of the 3rd international conference on genetic algorithms (ICGA-89). Morgan Kaufmann, San Francisco, CA, pp 328–333

Sharkey AJC (1996) On combining artificial neural nets. Connection Sci 8(3–4):299–313CrossRef

Sharpe PK, Glover RP (1999) Efficient GA based techniques for classification. Appl Intell 11:277–284CrossRef

Sirlantzis K, Fairhurst MC, Hoque MS (2001) Genetic algorithms for multi-classifier system configuration: a case study in character recognition. In: Kittler J, Roli F (eds) Proceedings of the 2nd international workshop on multiple classifier systems, Lecture notes in computer science, vol 2096. Springer, Berlin, pp 99–108. See Kuncheva (2004a) p.321CrossRef

Smith JE (2007) Coevolving memetic algorithms: a review and progress report. IEEE Trans Syst Man Cybern B Cybern 37(1):6–17CrossRef

Smith MG, Bull L (2005) Genetic programming with a genetic algorithm for feature construction and selection. GP and Evol Machines 6(3):265–281

Smith RE (1992) A report on the first international workshop on learning classifier systems (IWLCS-92). NASA Johnson Space Center, Houston, Texas, Oct. 6–9. ftp://lumpi.informatik.uni-dortmund.de/pub/LCS/papers/lcs92.ps.gz or from ENCORE, The Electronic Appendix to the Hitch-Hiker's Guide to Evolutionary Computation (ftp://ftp.krl.caltech.edu/pub/EC/Welcome.html) in the section on Classifier Systems

Smith RE (1994) Memory exploitation in learning classifier systems. Evolut Comput 2(3):199–220CrossRef

Smith RE, Cribbs HB (1994) Is a learning classifier system a type of neural network? Evolut Comput 2(1):19–36CrossRef

Smith RE, Goldberg DE (1991) Variable default hierarchy separation in a classifier system. In: Rawlins GJE (ed) Proceedings of the first workshop on foundations of genetic algorithms. Morgan Kaufmann, San Mateo, pp 148–170

Smith RE, Cribbs III HB (1997) Combined biological paradigms. Robot Auton Syst 22(1):65–74CrossRef

Song D, Heywood MI, Zincir-Heywood AN (2005) Training genetic programming on half a million patterns: an example from anomaly detection. IEEE Trans Evolut Comput 9(3):225–239CrossRef

Srinivas N, Deb K (1994) Multi-objective function optimization using non-dominated sorting genetic algorithm. Evolut Comput 2(3):221–248CrossRef

Stagge P (1998) Averaging efficiently in the presence of noise. In: Parallel problem solving from nature, vol 5. pp 188–197

Stolzmann W (1996) Learning classifier systems using the cognitive mechanism of anticipatory behavioral control, detailed version. In: Proceedings of the first European workshop on cognitive modelling. Berlin, TU, pp 82–89

Stone C, Bull L (2003) For real! XCS with continuous-valued inputs. Evolut Comput 11(3):298–336CrossRef

Storn R, Price K (1996) Minimizing the real functions of the ICEC'96 contest by differential evolution. In: Proceedings of the IEEE international conference Evolutionary Computation. IEEE Press, Washington, DC, pp 842–844

Stout M, Bacardit J, Hirst JD, Krasnogor N (2008) Prediction of recursive convex hull class assignment for protein residues. Bioinformatics 24(7):916–923CrossRef

Sutton RS (1986) Two problems with backpropagation and other steepest-descent learning procedures for networks. In: Proceedings of the 8th annual conference cognitive science society. Erlbaum, pp 823–831

Sziranyi T (1996) Robustness of cellular neural networks in image deblurring and texture segmentation. Int J Circuit Theory App 24(3):381–396CrossRef

Tamaddoni-Nezhad A, Muggleton SH (2000) Searching the subsumption lattice by a genetic algorithm. In: Cussens J, Frisch A (eds) Proceedings of the 10th international conference on inductive logic programming. Springer, Berlin, pp 243–252

Tamaddoni-Nezhad A, Muggleton S (2003) A genetic algorithms approach to ILP. In: Inductive logic programming, Lecture notes in computer science, vol 2583. Springer, Berlin, pp 285–300

Tharakannel K, Goldberg D (2002) XCS with average reward criterion in multi-step environment. Technical report, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign

Thompson S (1998) Pruning boosted classifiers with a real valued genetic algorithm. In: Research and development in expert systems XV – proceedings of ES'98. Springer, Berlin, pp 133–146

Thompson S (1999) Genetic algorithms as postprocessors for data mining. In: Data mining with evolutionary algorithms: research directions – papers from the AAAI workshop, Tech report WS–99–06. AAAI Press, Menlo Park, CA, pp 18–22

Thrift P (1991) Fuzzy logic synthesis with genetic algorithms. In: Booker LB, Belew RK (eds) Proceedings of 4th international conference on genetic algorithms (ICGA'91). Morgan Kaufmann, San Francisco, CA, pp 509–513

Tomlinson A (1999) Corporate classifier systems. PhD thesis, University of the West of England

Tomlinson A, Bull L (1998) A corporate classifier system. In: Eiben AE, Bäck T, Shoenauer M, Schwefel H-P (eds) Proceedings of the fifth international conference on parallel problem solving from nature – PPSN V, Lecture notes in computer science, vol 1498. Springer, Berlin, pp 550–559CrossRef

Tomlinson A, Bull L (2002) An accuracy-based corporate classifier system. J Soft Comput 6(3–4):200–215CrossRefMATH

Tran TH, Sanza C, Duthen Y, Nguyen TD (2007) XCSF with computed continuous action. In: Genetic and evolutionary computation conference (GECCO 2007). ACM, New York, pp 1861–1869

Tumer K, Ghosh J (1996) Analysis of decision boundaries in linearly combined neural classifiers. Pattern Recogn 29(2):341–348CrossRef

Turney P (1996) How to shift bias: lessons from the Baldwin effect. Evolut Comput 4(3):271–295CrossRef

Valentini G, Masulli F (2002) Ensembles of learning machines. In: WIRN VIETRI 2002: Proceedings of the 13th Italian workshop on neural nets-revised papers. Springer, Berlin, pp 3–22

Valenzuela-Rendón M (1989) Two analysis tools to describe the operation of classifier systems. PhD thesis, University of Alabama. Also TCGA technical report 89005

Valenzuela-Rendón M (1991) The fuzzy classifier system: a classifier system for continuously varying variables. In: Booker LB, Belew RK (eds) Proceedings of the 4th international conference on genetic algorithms (ICGA'91). Morgan Kaufmann, San Francisco, CA, pp 346–353

Valenzuela-Rendón M (1998) Reinforcement learning in the fuzzy classifier system. Expert Syst Appl 14:237–247CrossRef

Vallim R, Goldberg D, Llorà X, Duque T, Carvalho A (2003) A new approach for multi-label classification based on default hierarchies and organizational learning. In: Proceedings of the genetic and evolutionary computation conference, workshop sessions: learning classifier systems. ACM, New York, pp 2017–2022

Vanneschi L, Poli R (2012) Genetic programming: introduction, applications, theory and open issues. In: Rozenberg G, Bäck T, Kok J (eds) Handbook of natural computing. Springer, Berlin

Venturini G (1993) SIA: a supervised inductive algorithm with genetic search for learning attributes based concepts. In: Brazdil PB (ed) ECML-93 - Proceedings of the European conference on machine learning. Springer, Berlin, pp 280–296CrossRef

Vilalta R, Drissi Y (2002) A perspective view and survey of meta-learning. Artif Intell Rev 18(2):77–95CrossRef

Wada A, Takadama K, Shimohara K, Katai O (2005c) Learning classifier systems with convergence and generalization. In: Bull L, Kovacs T (eds) Foundations of learning classifier systems. Springer, Berlin, pp 285–304CrossRef

Wada A, Takadama K, Shimohara K (2005a) Counter example for Q-bucket-brigade under prediction problem. In: GECCO Workshops 2005. ACM, New York, pp 94–99

Wada A, Takadama K, Shimohara K (2005b) Learning classifier system equivalent with reinforcement learning with function approximation. In: GECCO Workshops 2005. ACM, New York, pp 92–93

Wada A, Takadama K, Shimohara K (2007) Counter example for Q-bucket-brigade under prediction problem. In: Kovacs T, LLòra X, Takadama K, Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems. International workshops, IWLCS 2003-2005, revised selected papers, Lecture notes in computer science, vol 4399. Springer, Berlin, pp 128–143

Whitley D, Goldberg D, Cantú-Paz E, Spector L, Parmee I, Beyer HG (eds) (2000) Proceedings of the genetic and evolutionary computation conference (GECCO-2000). Morgan Kaufmann, San Francisco, CA

Whiteson S, Stone P (2006) Evolutionary function approximation for reinforcement learning. J Mach Learn Res 7:877–917MathSciNetMATH

Whitley D, Starkweather T, Bogart C (1990) Genetic algorithms and neural networks: optimizing connections and connectivity. Parallel Comput 14(3):347–361CrossRef

Whitley D, Gordon VS, Mathias K (1994) Lamarckian evolution, the Baldwin effect and function optimization. In: Parallel problem solving from nature (PPSN-III). Springer, Berlin, pp 6–15

Wilcox JR (1995) Organizational learning within a learning classifier system. Master's thesis, University of Illinois. Also Technical Report No. 95003 IlliGAL

Wilson SW (2001a) Mining oblique data with XCS. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, third international workshop, IWLCS 2000, Lecture notes in computer science, vol 1996. Springer, Berlin, pp 158–176CrossRef

Wilson SW (1989) Bid competition and specificity reconsidered. Complex Syst 2:705–723

Wilson SW (1994) ZCS: a zeroth level classifier system. Evolut Comput 2(1):1–18. http://prediction-dynamics.com/

Wilson SW (1995) Classifier fitness based on accuracy. Evolut Comput 3(2):149–175. http://prediction-dynamics.com/

Wilson SW (1998) Generalization in the XCS classifier system. In: Koza JR, Banzhaf W, Chellapilla K, Deb K, Dorigo M, Fogel DB, Garzon MH, Goldberg DE, Iba H, Riolo R (eds) Genetic programming 1998: proceedings of the third annual conference, Morgan Kaufmann, San Francisco, CA, pp 665–674. http://prediction-dynamics.com/

Wilson SW (1999) Get real! XCS with continuous-valued inputs. In: Booker L, Forrest S, Mitchell M, Riolo RL (eds) Festschrift in honor of John H. Holland. Center for the Study of Complex Systems. pp 111–121. http://prediction-dynamics.com/

Wilson SW (2000) Mining oblique data with XCS. In: Proceedings of the international workshop on learning classifier systems (IWLCS-2000), in the joint workshops of SAB 2000 and PPSN 2000. Extended abstract

Wilson SW (2001b) Function approximation with a classifier system. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt HM, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO-2001). Morgan Kaufmann, San Francisco, CA, pp 974–981

Wilson SW (2002a) Classifiers that approximate functions. Natural Comput 1(2–3):211–234CrossRefMATH

Wilson SW (2002b) Compact rulesets from XCSI. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, Lecture notes in artificial intelligence, vol 2321. Springer, Berlin, pp 196–208

Wilson SW (2007) Three architectures for continuous action. In: Kovacs T, LLòra X, Takadama K, Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems. International workshops, IWLCS 2003-2005, revised selected papers, Lecture notes in computer science, vol 4399. Springer, Berlin, pp 239–257

Wilson SW (2008) Classifier conditions using gene expression programming. In: Bacardit J, Bernadó-Mansilla E, Butz M, Kovacs T, Llorà X, Takadama K (eds) Learning classifier systems. 10th and 11th international workshops (2006–2007), Lecture notes in computer science, vol 4998. Springer, Berlin, pp 206–217

Wilson SW, Goldberg DE (1989) A critical review of classifier systems. In: Schaffer JD (ed) Proceedings of the 3rd international conference on genetic algorithms. Morgan Kaufmann, San Francisco, CA, pp 244–255. http://prediction-dynamics.com/

Wolpert DH (1996) The lack of a priori distinctions between learning algorithms. Neural Comput 8(7):1341–1390CrossRef

Wong ML, Leung KS (2000) Data mining using grammar based genetic programming and applications. Kluwer, NorwellMATH

Woods K, Kegelmeyer W, Bowyer K (1997) Combination of multiple classifiers using local accuracy estimates. IEEE Trans Pattern Anal Mach Intell 19:405–410CrossRef

Woodward JR (2003) GA or GP? That is not the question. In: Proceedings of the 2003 congress on evolutionary computation, CEC2003. IEEE Press, Washington DC, pp 1056–1063

Yamasaki K, Sekiguchi M (2000) Clear explanation of different adaptive behaviors between Darwinian population and Larmarckian population in changing environment. In: Proceedings of the fifth international symposium on artificial life and robotics. pp 120–123

Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423–1447CrossRef

Yao X, Islam MM (2008) Evolving artificial neural network ensembles. IEEE Comput Intell Mag 3(1):31–42CrossRef

Yao X, Liu Y (1997) A new evolutionary system for evolving artificial neural networks. IEEE Trans Neural Networ 8:694–713CrossRef

Yao X, Liu Y (1998) Making use of population information in evolutionary artificial neural networks. IEEE Trans Syst Man Cybern B 28(3):417–425MathSciNet

Zatuchna ZV (2005) AgentP: a learning classifier system with associative perception in maze environments. PhD thesis, University of East Anglia

Zatuchna ZV (2004) AgentP model: Learning Classifier System with Associative Perception. In 8th parallel problem solving from nature international conference (PPSN VIII). pp 1172–1182

Zhang B-T, Veenker G (1991) Neural networks that teach themselves through genetic discovery of novel examples. In: Proceedings 1991 IEEE international joint conference on neural networks (IJCNN'91) vol 1. IEEE Press, Washington DC, pp 690–695

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