Abstract
This is a survey of the field of genetics-based machine learning (GBML): the application of evolutionary algorithms (ES) to machine learning. We assume readers are familiar with evolutionary algorithms and their application to optimization problems, but not necessarily with machine learning. We briefly outline the scope of machine learning, introduce the more specific area of supervised learning, contrast it with optimization and present arguments for and against GBML. Next we introduce a framework for GBML, which includes ways of classifying GBML algorithms and a discussion of the interaction between learning and evolution. We then review the following areas with emphasis on their evolutionary aspects: GBML for subproblems of learning, genetic programming, evolving ensembles, evolving neural networks, learning classifier systems, and genetic fuzzy systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abbass HA (2003) Speeding up backpropagation using multiobjective evolutionary algorithms. Neural Comput, 15(11):2705–2726
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):324–331
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):249–268
Angeline PJ, Sauders GM, Pollack JB (1994) An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans Neural Netw 5:54–65
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 17–46
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 115–132
Bernadó-Mansilla E, Garrell-Guiu JM (2003) Accuracy-based learning classifier systems: models, analysis and applications to classification tasks. Evolut Comput 11(3):209–238
Bernadó-Mansilla E, Ho TK (2005) Domain of competence of XCS classifier system in complexity measurement space. IEEE Trans Evolut Comput 9(1):82–104
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):123–140
Breiman L (1998) Arcing classifiers. Ann Stat 26(3):801–845
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):5–20
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 11:496–502
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):8–46
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 211–229
Butz MV (2002b) Anticipatory learning classifier systems. Kluwer, Norwell, MA
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 253–272
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 1835–1842
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):452–473
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 1457–1464
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 1449–1456
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 655–662
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):345–380
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):54–68
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 15:536–550
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):2884–2905
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):417–445
Chandra A, Yao X (2006b) Evolving hybrid ensembles of learning machines for better generalisation. Neurocomputing 69(7–9):686–700
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 103:339–347
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, Singapore
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):26–39
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 3:161–188
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):29–74
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):381–395
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 1851–1858
Drugowitsch J (2008) Design and analysis of learning classifier systems: a probabilistic approach. Springer, Berlin
Drugowitsch J, Barry A (2007) A formal framework and extensions for function approximation in learning classifier systems. Mach Learn 70(1):45–88
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):47–62
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, Berlin
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 8:129–164
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 1882–1893
Gerard P, Stolzmann W, Sigaud O (2002) YACS, a new learning classifier system using anticipation. J Soft Comput 6(3–4):216–228
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 3:375–416
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, MA
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 13:229–257
Greene DP, Smith SF (1994) Using coverage as a model building constraint in learning classifier systems. Evolut Comput 2(1):67–91
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):151–183
Hanebeck D, Schmidt K (1996) Genetic optimization of fuzzy networks. Fuzzy set syst 79:59–68
Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12(10): 993–1001
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):27–46
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 3–32
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):129–139
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 1977–1984
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 1389–1396
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):109–119
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 942–951
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 14:820–834
Jain A, Zongker D (1997) Feature selection: evaluation, application and small sample performance. IEEE Trans. Pattern Anal Mach Intell 19(2):153–158
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 13:189–228
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, Berlin
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 4:461–476
Kolman E, Margaliot M (2009) Knowledge-based neurocomputing: a fuzzy logic approach, Studies in fuzziness and soft computing, vol 234. Springer, Berlin
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 143–160
Kovacs T (2004) Strength or accuracy: credit assignment in learning classifier systems. Springer, Berlin
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 80–99
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, MA
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):474–488
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):53–76
Krawiec K (2002) Genetic programming-based construction of features for machine learning and knowledge discovery tasks. GP and Evol Machines 3(4):329–343
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 33:25–41
Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, Hoboken
Kushchu I (2002) An evaluation of evolutionary generalization in genetic programming. Artif Intell Rev 18(1):3–14
Lam L, Suen CY (1995) Optimal combination of pattern classifiers. Pattern Recogn Lett 16:945–954 See Kuncheva (2004a) p.167
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):162–170
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):63–82
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 33–62
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, Berlin
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 1814–1821
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 1822–1829
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 1481–1488
Liangjie Z, Yanda L (1996) A new global optimizing algorithm for fuzzy neural networks. Int J Elect 80(3):393–403
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):367–386
Liu Y, Yao X (1999) Ensemble learning via negative correlation. Neural Networ 12:1399–1404
Liu Y, Yao X, Higuchi T (2000) Evolutionary ensembles with negative correlation learning. IEEE Trans Evolut Comput 4(4):380–387
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 88–89
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 596–603
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 1819–1826
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):748–768
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):453–461
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):141–152
Opitz D, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169–198
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 2871–2874
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 1838–1845
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 1838–1845
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 1846–1853
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):209–232
Pal S, Bhandari D (1994) Genetic algorithms with fuzzy fitness function for object extraction using cellular networks. Fuzzy Set Syst 65(2–3):129–139
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):301–312
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):164–171
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, Berlin
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.321
Sánchez L, Couso I (2007) Advocating the use of imprecisely observed data in genetic fuzzy systems. IEEE Trans Fuzzy Syst 15(4):551–562
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 223–242
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):299–313
Sharpe PK, Glover RP (1999) Efficient GA based techniques for classification. Appl Intell 11:277–284
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.321
Smith JE (2007) Coevolving memetic algorithms: a review and progress report. IEEE Trans Syst Man Cybern B Cybern 37(1):6–17
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):199–220
Smith RE, Cribbs HB (1994) Is a learning classifier system a type of neural network? Evolut Comput 2(1):19–36
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):65–74
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):225–239
Srinivas N, Deb K (1994) Multi-objective function optimization using non-dominated sorting genetic algorithm. Evolut Comput 2(3):221–248
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):298–336
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):916–923
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):381–396
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 550–559
Tomlinson A, Bull L (2002) An accuracy-based corporate classifier system. J Soft Comput 6(3–4):200–215
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):341–348
Turney P (1996) How to shift bias: lessons from the Baldwin effect. Evolut Comput 4(3):271–295
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 14:237–247
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 280–296
Vilalta R, Drissi Y (2002) A perspective view and survey of meta-learning. Artif Intell Rev 18(2):77–95
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 285–304
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 7:877–917
Whitley D, Starkweather T, Bogart C (1990) Genetic algorithms and neural networks: optimizing connections and connectivity. Parallel Comput 14(3):347–361
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 158–176
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):211–234
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):1341–1390
Wong ML, Leung KS (2000) Data mining using grammar based genetic programming and applications. Kluwer, Norwell
Woods K, Kegelmeyer W, Bowyer K (1997) Combination of multiple classifiers using local accuracy estimates. IEEE Trans Pattern Anal Mach Intell 19:405–410
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):1423–1447
Yao X, Islam MM (2008) Evolving artificial neural network ensembles. IEEE Comput Intell Mag 3(1):31–42
Yao X, Liu Y (1997) A new evolutionary system for evolving artificial neural networks. IEEE Trans Neural Networ 8:694–713
Yao X, Liu Y (1998) Making use of population information in evolutionary artificial neural networks. IEEE Trans Syst Man Cybern B 28(3):417–425
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
Acknowledgments
Thanks to my editor Thomas Bäck for his patience and encouragement, and to Larry Bull, John R. Woodward, Natalio Krasnogor, Gavin Brown and Arjun Chandra for comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Glossary
- EA
-
Evolutionary Algorithm
- FRBS
-
Fuzzy Rule-Based System
- GA
-
Genetic Algorithm
- GBML
-
Genetics-Based Machine Learning
- GFS
-
Genetic Fuzzy System
- GP
-
Genetic Programming
- LCS
-
Learning Classifier System
- NN
-
Neural Network
- SL
-
Supervised Learning
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this entry
Cite this entry
Kovacs, T. (2012). Genetics-Based Machine Learning. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-92910-9_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92909-3
Online ISBN: 978-3-540-92910-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering