%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 %% gbml.bib %% %% This is a bibliography of papers on genetics-based machine %% learning. It's a subset of papers which appear in: %% %% Tim Kovacs. Genetics-based Machine Learning. To appear in %% Grzegorz Rozenberg, Thomas Baeck, and Joost Kok, editors, %% Handbook of Natural Computing: Theory, Experiments, and Applications, %% Springer 2010 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Other chapters in the handbook of natural computing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 @Book{Rozenberg2009, editor = {Grzegorz Rozenberg and Thomas B\"{a}ck and Joost Kok}, title = {Handbook of Natural Computing: Theory, Experiments, and Applications}, publisher = {Springer Verlag}, year = {2010}, } % in 2.9 they cite [56] as defining a set of typical GP problems. They probably mean [57]. @InCollection{Vanneschi2009, author = {Leonardo Vanneschi and Riccardo Poli}, title = {Genetic Programming: Introduction, Applications, Theory and Open Issues}, booktitle = {Handbook of Natural Computing: Theory, Experiments, and Applications}, editor = {Grzegorz Rozenberg and Thomas B\"{a}ck and Joost Kok}, publisher = {Springer Verlag}, year = {2010}, } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Baldwin effect % see also bib at http://www.alife.cs.is.nagoya-u.ac.jp/~reiji/baldwin/ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 @Article{Turney1996a, author = {Peter Turney and and Darrell Whitley and Russell Anderson (editors)}, title = {Special Issue on the Baldwin Effect}, journal = {Evolutionary Computation}, publisher = {MIT Press}, year = {1996}, volume = {4}, number = {3}, } % introduction to special issue @Article{Turney1996b, author = {Peter Turney and and Darrell Whitley and Russell Anderson}, title = {Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect}, journal = {Evolutionary Computation}, year = {1996}, volume = {4}, number = {3}, pages = {iv--viii}, } @Article{Turney1996c, author = {Peter Turney}, title = {How to Shift Bias: Lessons from the Baldwin Effect }, journal = {Evolutionary Computation}, year = {1996}, volume = {4}, number = {3}, pages = {271--295}, } @Book{Belew1996a, editor = {R.K. Belew and M. Mitchell}, title = {Adaptive Individuals in Evolving Populations: Models and Algorithms}, publisher = {Addison-Wesley}, year = {1996}, } @Book{Weber2007a, editor = {Bruce H. Weber and David J. Depew}, title = {Evolution and Learning: The Baldwin Effect Reconsidered}, publisher = {MIT Press}, year = {2007}, } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Darwinian vs. Lamarckian evolution % See section 4 of "Neuroevolution: from architectures to learning" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 @InProceedings{Ackley1992, author = {D.H. Ackley and M.L. Littman}, title = {Interactions between learning and evolution}, booktitle = {Artificial Life II: Santa Fe Institute Studies in the Sciences of Complexity}, pages = {487--509}, year = {1992}, editor = {C. Langton and C. Taylor and S. Rasmussen and J. Farmer}, volume = {10}, publisher = {Addison Wesley}, } @InProceedings{Sasaki1997, author = {T. Sasaki and M. Tokoro}, title = {Adaptation toward changing environments: Why Darwinian in nature?}, booktitle = {Proceedings of the 4th European conference on artificial life}, pages = {145--153}, year = {1997}, editor = {P. Husbands and I. Harvey}, publisher = {MIT Pess}, } @InProceedings{Pereira2001, author = {F.B. Pereira and E. Costa}, title = {Understanding the role of learning in the evolution of busy beaver: A comparison between the {B}aldwin {E}ffect and {L}amarckian strategy}, booktitle = {Proc. of the Genetic and Evol. Computation Conf. (GECCO--2001)}, pages = {884--891}, year = {2001}, } @InProceedings{Yamasaki2000, author = {K. Yamasaki and M. Sekiguchi}, title = {Clear explanation of different adaptive behaviors between {D}arwinian Population and {L}armarckian population in changing environment}, booktitle = {Proc. Fifth Int. Symp. on Artificial Life and Robotics}, pages = {120--123}, year = {2000}, } @InProceedings{Whitley1994, author = {Darrell Whitley and V. Scott Gordon and Keith Mathias}, title = {Lamarckian evolution, the {B}aldwin effect and function optimization}, booktitle = {Parallel Problem Solving from Nature (PPSN-III)}, year = {1994}, pages = {6--15}, publisher = {Springer-Verlag}, editors = {Yuval Davidor and Hans-Paul Schwefel and Reinhard M\"{a}nner} } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Learning and Evolution %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 @InProceedings{Fogarty1989, author = {T.C. Fogarty}, title = {An incremental genetic algorithm for real-time learning}, booktitle = {Proc. Sixth Int. Workshop on Machine Learning}, pages = {416--419}, year = {1989}, } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Memetic Algorithms %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % From wikipedia page on Memetic Algorithms: "For a review on second generation MA, i.e., MA % considering multiple individual learning methods within an evolutionary system, the reader % is referred to @Article{Ong2006, author = {Y.S. Ong and M.H. Lim and N. Zhu and K.W. Wong}, title = {Classification of Adaptive Memetic Algorithms: A Comparative Study}, journal = {IEEE Transactions on Systems Man and Cybernetics -- Part {B}}, year = {2006}, volume = {36}, number = {1}, pages = {141--152}, } @Article{Ong2007, author = {Y.-S. Ong and N. Krasnogor and H. Ishibuchi (editors)}, title = {Special Issue on Memetic Algorithms}, journal = {IEEE Transactions on Systems, Man and Cybernetics - Part B}, publisher = {IEEE}, year = {2007}, volume = {37}, number = {1}, } @Article{Ong2009, author = {Yew-Soon Ong and Meng-Hiot Lim and Ferrante Neri and Hisao Ishibuchi}, title = {Special issue on Memetic Algorithms}, journal = {Soft Computing}, publisher = {Springer}, volume = {13}, number = {8-9}, year = {2009}, } @Article{Hart2004, author = {W.E. Hart and N. Krasnogor and J.E. Smith (editors)}, title = {Special Issue on Memetic Algorithms}, journal = {Evolutionary Computation}, publisher = {MIT Press}, year = {2004}, volume = {12}, number = {3}, } @InCollection{Hart2004a, author = {W.E. Hart and N. Krasnogor and J.E. Smith}, title = {Memetic Evolutionary Algorithms}, booktitle = {Recent Advances in Memetic Algorithms}, pages = {3--30}, publisher = {Springer}, year = {2004}, editor = {Hart and Krasnogor and Smith}, } @Book{Hart2005, editor = {William E. Hart and N. Krasnogor and J.E. Smith}, title = {Recent Advances in Memetic Algorithms}, series = {Studies in Fuzziness and Soft Computing}, publisher = {Springer}, year = {2005}, volume = {166}, ISBN = {978-3-540-22904-9}, } @PhdThesis{Krasnogor2002a, author = {N. Krasnogor}, title = {Studies on the Theory and Design Space of Memetic Algorithms}, school = {University of the West of England}, year = {2002}, } @Article{Krasnogor2004a, author = {Natalio Krasnogor}, title = {Self-generating metaheuristics in bioinformatics: the protein structure comparison case}, journal = {Genetic Programming and Evolvable Machines}, year = {2004}, volume = {5}, number = {2}, pages = {181--201}, } @Article{Krasnogor2004b, author = {Natalio Krasnogor and S. Gustafson}, title = {A study on the use of self-generation in memetic algorithms}, journal = {Natural Computing}, year = {2004}, volume = {3}, number = {1}, pages = {53--76}, } @Article{Krasnogor2005a, author = {N. Krasnogor and J.E. Smith}, title = {A tutorial for competent memetic algorithms: model, taxonomy and design issues}, journal = {IEEE Transactions on Evolutionary Computation}, year = {2005}, volume = {9}, number = {5}, pages = {474--488}, } @Article{Smith2007a, author = {J.E. Smith}, title = {Coevolving Memetic Algorithms: A Review and Progress Report}, journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics}, year = {2007}, volume = {37}, number = {1}, pages = {6--17}, } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Genetic Fuzzy Systems %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 @Article{Herrera2008a, author = {Francisco Herrera}, title = {Genetic fuzzy systems: taxonomy, current research trends and prospects}, journal = {Evolutionary Intelligence}, year = {2008}, volume = {1}, number = {1}, pages = {27--46}, } @Article{Sanchez2007a, author = {L. S\'{a}nchez and I. Couso}, title = {Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems }, journal = {IEEE Transactions on Fuzzy Systems}, year = {2007}, volume = {15}, number = {4}, pages = {551--562}, } % seminal paper on genetic tuning of DB @Article{Karr1991a, author = {C. Karr}, title = {Genetic algorithms for fuzzy controllers}, journal = {AI Expert}, year = {1991}, volume = {6}, number = {2}, pages = {26--33}, } % seminal paper on Relational matrix-based FRBS learning (see Herrera2008a) @Article{Pham1991a, author = {D.T. Pham and D. Karaboga}, title = {Optimum design of fuzzy logic controllers using genetic algorithms}, journal = {J. Systems Eng}, year = {1991}, volume = {1}, number = {}, pages = {114--118}, } %% Genetic Neuro-fuzzy systems @Article{Liangjie1996a, author = {Z. Liangjie and L. Yanda}, title = {A new global optimizing algorithm for fuzzy neural networks}, journal = {Int. J. Electronics}, year = {1996}, volume = {80}, number = {3}, pages = {393--403}, } @Article{Haneback1996a, author = {D. Hanebeck and K. Schmidt}, title = {Genetic optimization of fuzzy networks}, journal = {Fuzzy sets and systems}, year = {1996}, volume = {79}, pages = {59--68}, } @Article{Perneel1995a, author = {Christiaan Perneel and Jean-Marc Themlin}, title = {Optimization of fuzzy expert systems using genetic algorithms and neural networks}, journal = {{IEEE Trans. on fuzzy systems}}, year = {1995}, volume = {3}, number = {3}, pages = {301--312}, } @InProceedings{He1999a, author = {Lin He and Ke-jun Wang and Hong-zhang Jin and Guo-bin Li and X.Z. Gao}, title = {The Combination and Prospects of Neural Networks, Fuzzy Logic and Genetic Algorithms}, booktitle = {IEEE Midnight-Sun Workshop on Soft Computing Methods in Industrial Applications}, pages = {52--57}, year = {1999}, publisher = {IEEE}, } @Article{Linkens1996a, author = {D.A. Linkens and H.O. Nyongesa}, title = {Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications}, journal = {IEE Proceedings - Control Theory and Applications}, year = {1996}, volume = {143}, number = {4}, pages = {367--386}, abstract = {In designing controllers for complex dynamical systems there are needs that are not sufficiently addressed by conventional control theory. These relate mainly to the problem of environmental uncertainty and often call for human-like decision making requiring the use of heuristic reasoning and learning from past experience. Learning is required when the complexity of a problem or the uncertainty thereof prevents a priori specification of a satisfactory solution. Such solutious are then only possible through accumulating information about the problem and using this information to dynamically generate an acceptable solution. Such systems can be referred to as intelligent control systems. In recent years, `intelligent control' has come to embrace diverse methodologies combining conventional control theory and emergent techniques based on physiological metaphors, such as neural networks, fuzzy logic, artificial intelligence, genetic algorithms and a wide variety of search and optimisation techniques. The paper reviews aspects of these emergent techniques, in particular, fuzzy logic, neural networks and genetic algorithms that pertain to realisation of intelligent control systems. The fundamental concepts and design techniques of each paradigm are dicussed, providing a compact reference for their application.}, } @Article{Mitra2000a, author = {Sushmita Mitra and Yoichi Hayashi}, title = {Neuro–Fuzzy Rule Generation: Survey in Soft Computing Framework}, journal = {IEEE Transactions on Neural Networks}, year = {2000}, volume = {11}, number = {3}, pages = {748--768}, } @Book{Kolman2009a, author = {Eyal Kolman and Michael Margaliot}, title = {Knowledge-Based Neurocomputing: A Fuzzy Logic Approach}, publisher = {Springer}, year = {2009}, volume = {234}, series = {Studies in Fuzziness and Soft Computing}, abstract = {In this monograph, the authors introduce a novel fuzzy rule-base, referred to as the Fuzzy All-permutations Rule-Base (FARB). They show that inferring the FARB, using standard tools from fuzzy logic theory, yields an input-output map that is mathematically equivalent to that of an artificial neural network. Conversely, every standard artificial neural network has an equivalent FARB. The FARB-ANN equivalence integrates the merits of symbolic fuzzy rule-bases and sub-symbolic artificial neural networks, and yields a new approach for knowledge-based neurocomputing in artificial neural networks.}, } @Article{Homaifar1995a, author = {A. Homaifar and E. Mccormick}, title = {Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms}, journal = {IEEE Trans. Fuzzy. Syst.}, year = {1995}, volume = {3}, number = {2}, pages = {129--139}, } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Evolutionary Intelligence (Journal), Vol. 1 Number 1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 @Book{Bull2008, editor = {Larry Bull}, title = {Evolutionary Intelligence}, publisher = {Springer}, year = {2008}, volume = {1}, number = {1}, } @Article{Floreano2008, author = {Dario Floreano and Peter D\"{u}rr and Claudio Mattiussi}, title = {Neuroevolution: from architectures to learning}, journal = {Evolutionary Intelligence}, year = {2008}, volume = {1}, number = {1}, pages = {47--62}, } @Article{, author = {J. Timmis and P. Andrews and N. Owens and E. Clark}, title = {An interdisciplinary perspective on artificial immune systems}, journal = {Evolutionary Intelligence}, year = {2008}, volume = {1}, number = {1}, pages = {5--26}, } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Evolving NNs %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 @Article{Yao1997, author = {X. Yao and Y. Liu}, title = {A new evolutionary system for evolving artificial neural networks}, journal = {IEEE Trans. Neural Networks}, year = {1997}, volume = {8}, pages = {694--713}, } @Article{Yao1998a, author = {X. Yao and Y. Liu}, title = {Making use of population information in evolutionary artificial neural networks}, journal = {IEEE Transactions on Systems, Man and Cybernetics B}, year = {1998}, volume = {28}, number = {3}, pages = {417--425}, } @article{Yao1999a, title={{Evolving artificial neural networks}}, author={Yao, X.}, journal={Proceedings of the IEEE}, volume={87}, number={9}, pages={1423--1447}, year={1999} } @Article{Yao2008a, author = {X. Yao and M.M. Islam}, title = {Evolving artificial neural network ensembles}, journal = {IEEE Computational Intelligence Magazine}, year = {2008}, volume = {3}, number = {1}, pages = {31--42}, } @Article{Islam2003a, author = {M.M. Islam and X. Yao and K. Murase}, title = {A constructive algorithm for training cooperative neural network ensembles}, journal = {IEEE Transactions on Neural Networks}, year = {2003}, volume = {14}, pages = {820--834}, } @Article{Andersen1993, author = {H.C. Andersen and A.C. Tsoi}, title = {A constructive algorithm for the training of a multi-layer perceptron based on the genetic algorithm}, journal = {Complex Systems}, year = {1993}, volume = {7}, number = {4}, pages = {249--268}, } @InProceedings{Sutton1986, author = {R.S. Sutton}, title = {Two problems with backpropagation and other steepest-descent learning procedures for networks}, booktitle = {Proc. 8th Annual Conf. Cognitive Science Society}, pages = {823--831}, year = {1986}, publisher = {Erlbaum}, } @Article{Whitley1990, author = {D. Whitley and T. Starkweather and C. Bogart}, title = {Genetic algorithms and neural networks: Optimizing connections and connectivity}, journal = {Parallel Comput.}, year = {1990}, volume = {14}, number = {3}, pages = {347--361}, } @Article{Angeline1994, author = {P.J. Angeline and G.M. Sauders and J.B. Pollack}, title = {An evolutionary algorithm that constructs recurrent neural networks}, journal = {IEEE Trans. Neural Networks}, year = {1994}, volume = {5}, pages = {54--65}, } @InProceedings{Miller1989, author = {G.F. Miller and P.M. Todd and S.U. Hegde}, title = {Designing neural networks using genetic algorithms}, booktitle = {Proc. 3rd Int. Conf. Genetic Algorithms and Their Applications}, pages = {379--384}, year = {1989}, editor = {J.D. Schaffer}, publisher = {Morgan Kaufmann}, } @InProceedings{Belew1992, author = {R.K. Belew and J. McInerney and N.N. Schraudolph}, title = {Evolving networks: using the genetic algorithm with connectionistic learning}, booktitle = {Proceedings of the 2nd Conference on Artificial Life}, pages = {51--548}, year = {1992}, editor = {C.G. Langton and C. Taylor and J.D. Farmer and S. Rasmussen}, publisher = {Addison-Wesley}, } @TechReport{Dasdan1993, author = {A. Dasdan and K. Oflazer}, title = {Genetic synthesis of unsupervised learning algorithms}, institution = {Department of Computer Engineering and Information Science, Bilkent University, Ankara}, year = {1993}, number = {BU-CEIS-9306}, } @Article{Kitano1990, author = {H. Kitano}, title = {Designing neural networks by genetic algorithms using graph generation system}, journal = {Journal of Complex System}, year = {1990}, volume = {4}, pages = {461--476}, } @Article{Gruau1995, author = {F. Gruau}, title = {Automatic definition of modular neural networks}, journal = {Adaptive Behavior}, year = {1995}, volume = {3}, number = {2}, pages = {151--183}, } @InProceedings{Nolfi1994, author = {S. Nolfi and O. Miglino and D. Parisi}, title = {Phenotypic plasticity in evolving neural networks}, booktitle = {From perception to action}, pages = {146--157}, year = {1994}, editor = {P. Gaussier and J.-D. Nicoud}, publisher = {IEEE Press}, } @InProceedings{Husbands1994, author = {P. Husbands and I. Harvey and D. Cliff and G. Miller}, title = {The use of genetic algorithms for the development of sensorimotor control systems}, booktitle = {From perception to action}, pages = {110--121}, year = {1994}, editor = {P. Gaussier and J.-D. Nicoud}, publisher = {IEEE Press}, } @Article{Pal1994, author = {S. Pal and D. Bhandari}, title = {Genetic algorithms with fuzzy fitness function for object extraction using cellular networks}, journal = {Fuzzy Sets and Systems}, year = {1994}, volume = {65}, number = {2--3}, pages = {129--139}, } @Article{Sziranyi1996, author = {T. Sziranyi}, title = {Robustness of cellular neural networks in image deblurring and texture segmentation}, journal = {Int. J. Circuit Theory App.}, year = {1996}, volume = {24}, number = {3}, pages = {381--396}, } @InProceedings{Chalmers1990, author = {D. Chalmers}, title = {The evolution of learning: An experiment in genetic connectionism}, booktitle = {Proc. 1990 Connectionist Models Summer School}, pages = {81--90}, year = {1990}, editor = {E. Touretsky}, publisher = {Morgan Kaufmann}, } @InCollection{Radi2003, author = {Amr Radi and Riccardo Poli}, editor = {Ajith Abraham and Lakhmi Jain and Janusz Kacprzyk}, title = {Discovering Efficient Learning Rules for Feedforward Neural Networks using Genetic Programming}, booktitle = {Recent Advances in Intelligent Paradigms and Applications}, publisher = {Springer Verlag}, year = {2003}, pages = {133--159}, } @Article{Castillo2007a, author = {P.A. Castilloa and J.J. Merelo and M.G. Arenas and G. Romero}, title = {Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters}, journal = {Information Sciences}, year = {2007}, volume = {177}, number = {14}, pages = {2884--2905}, } @Article{Liu1999a, author = {Y. Liu and X. Yao}, title = {Ensemble learning via negative correlation}, journal = {Neural Networks}, year = {1999}, volume = {12}, pages = {1399--1404}, } @Article{Liu2000b, author = {Y. Liu and X. Yao and T. Higuchi}, title = {Evolutionary ensembles with negative correlation learning}, journal = {IEEE Trans. on Evolutionary Computation}, year = {2000}, volume = {4}, number = {4}, pages = {380--387}, } @Article{Sharkey1996a, author = {A.J.C. Sharkey}, title = {On combining artificial neural nets}, journal = {Connection Science}, year = {1996}, volume = {8}, number = {3--4}, pages = {299--313}, } @Book{Kasabov2007a, author = {N. Kasabov}, title = {Evolving Connectionist Systems: The Knowledge Engineering Approach}, publisher = {Springer}, year = {2007}, } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Evolutionary rule extraction % http://sky.fit.qut.edu.au/~andrewsr/rulex.html % Not many of them around, and surveys by mainstream rule extraction % people don't mention any genetic methods %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 @Article{Zitar1995a, author = {R.A. Zitar and M.H. Hassoun}, title = {Neurocontrollers trained with rules extracted by a genetic assisted reinforcement learning system}, journal = {IEEE Transactions on Neural Networks}, year = {1995}, volume = {6}, number = {4}, pages = {859--879}, } @inproceedings{Santos2000b, author = {R. Santos and J.C. Nievola and A.A. Freitas}, title = {Extracting comprehensible rules from neural networks via genetic algorithms}, year = {2000}, pages = {130-139}, ISBN = {0-7803-6572-0}, booktitle = {Proc. 2000 IEEE Symp. on Combinations of Evolutionary Computation and Neural Networks (ECNN-2000)}, publisher = {IEEE}, } @InCollection{Markowska-Kaczmar2004a, author = {Urszula Markowska-Kaczmar and Pawelstrok Wnuk-Lipi\'{n}ski}, editor = {L. Rutkowski et. al}, title = {Artificial Intelligence and Soft Computing - ICAISC 2004}, booktitle = {Rule Extraction from Neural Network by Genetic Algorithm with Pareto Optimization}, publisher = {Springer}, year = {2004}, volume = {3070/2004}, series = {LNCS}, pages = {450--455}, } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Sub-problems of ML with evolution %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % table of contents available at google books @book{Freitas2002a, author = {A.A. Freitas}, title = {Data Mining and Knowledge Discovery with Evolutionary Algorithms}, year = {2002}, ISBN = {3-540-43331-7}, publisher = {Spinger-Verlag}, address = {Berlin}, } @InCollection{Freitas2002b, author = {A.A. Freitas}, title = {A survey of evolutionary algorithms for data mining and knowledge discovery}, booktitle = {Advances in Evolutionary Computation}, pages = {819--845}, publisher = {Springer-Verlag}, year = {2002}, editor = {A. Ghosh and S. Tsutsui}, abstract = {This chapter discusses the use of evolutionary algorithms, particularly genetic algorithms and genetic programming, in data mining and knowledge discovery. We focus on the data mining task of classification. In addition, we discuss some preprocessing and postprocessing steps of the knowledge discovery process, focusing on attribute selection and pruning of an ensemble of classifiers. We show how the requirements of data mining and knowledge discovery influence the design of evolutionary algorithms. In particular, we discuss how individual representation, genetic operators and fitness functions have to be adapted for extracting high-level knowledge from data.}, } @InProceedings{Martin-Bautista1999a, author = {M.J. Martin-Bautista and M.-A. Vila}, title = {A survey of genetic feature selection in mining issues}, booktitle = {{Proceedings of the Congress on Evolutionary Computation (CEC'99}}, pages = {1314--1321}, year = {1999}, publisher = {IEEE}, } % See Freitas2002a p.190 @Article{Sharpe1999a, author = {P.K. Sharpe and R.P. Glover}, title = {Efficient GA based techniques for classification}, journal = {Applied Intelligence}, year = {1999}, volume = {11}, pages = {277--284}, } % See Freitas2002a p.190 @Article{Kudo2000a, author = {M. Kudo and J. Skalansky}, title = {Comparison of algorithms that select features for pattern classifiers}, journal = {Pattern Recognition}, year = {2000}, volume = {33}, pages = {25--41}, } % input generation. cited in Yao1999 @InProceedings{Cho1996, author = {S. Cho and K. Cha}, title = {Evolution of neural net training set through addition of virtual samples}, booktitle = {Proc. 1996 IEEE Int. Conf. Evol. Comp., ICEC'96 }, pages = {685-688}, year = {1996}, publisher = {IEEE}, } % input generation. cited in Yao1999 @InProceedings{Zhang1991, author = {B.-T. Zhang and G. Veenker}, title = {Neural networks that teach themselves through genetic discovery of novel examples}, booktitle = {Proc. 1991 IEEE Int. Joint Conf. on Neural Networks (IJCNN'91)}, pages = {690-695}, year = {1991}, volume = {1}, publisher = {IEEE}, } @InProceedings{Romaniuk1994a, author = {S. Romaniuk}, title = {Towards minimal network architectures with evolutionary growth networks}, booktitle = {{Proc. IEEE Int. Conf. on NNs, IEEE World Congress on Computational Intelligence}}, pages = {1710--1713}, year = {1994}, volume = {3}, publisher = {IEEE}, } % fitness function approximation. cited in Yao1999. % This is a nice example of GBML in that it combines NN and genetic fuzzy systems. @Article{Morimoto1997, author = {T. Morimoto and J. Suzuki and Y. Hashimoto}, title = {Optimization of a fuzzy controller for fruit storage using neural networks and genetic algorithms}, journal = {Engineering Applications of Art. Int.}, year = {1997}, volume = {10}, number = {5}, pages = {453--461}, abstract = {It is difficult to determine membership functions and control rules efficiently when applying fuzzy control to an unknown system. In this study, a new fuzzy control technique, which efficiently selects optimal membership functions and control rules by using neural networks and genetic algorithms, was proposed and then applied to the control of relative humidity in a fruit-storage house. The control input is the on-off of ventilation. The response of relative humidity, as affected by ventilation, is first identified using neural networks, and then optimal membership functions and control rules are sought through simulation of the identified model using genetic algorithms. The neural network works as a simulator for the search for an optimal value. The control aim here is to maintain the relative humidity in the storage house at the desired value through the on-off control of ventilation by the fuzzy control. Results show that this control technique allowed optimal membership functions and control rules to be successfully determined, and its control performance was superior to the conventional control.} } @article{Whiteson2006, author = {Shimon Whiteson and Peter Stone}, title = {Evolutionary Function Approximation for Reinforcement Learning}, journal = {J. Mach. Learn. Res.}, volume = {7}, year = {2006}, issn = {1533-7928}, pages = {877--917}, publisher = {MIT Press}, } @InProceedings{Stagge1998, author = {Peter Stagge}, title = {Averaging efficiently in the presence of noise}, booktitle = {Parallel problem solving from nature}, pages = {188--197}, year = {1998}, volume = {5}, } @InProceedings{Beielstein2002, author = {Thomas Beielstein and Shandor Markon}, title = {Threshold selection, hypothesis tests and {DOE} methods}, booktitle = {2002 Congress on Evolutionary Computation}, pages = {777--782}, year = {2002}, } % tree is constructed heuristically and EAs (ES+GA) optimise the tests performed at the nodes @Article{Cantu-Paz2003a, author = {E. Cantu-Paz and C. Kamath}, title = {Inducing oblique decision trees with evolutionary algorithms}, journal = {IEEE Transactions on Evolutionary Computation}, year = {2003}, volume = {7}, number = {1}, pages = {54--68}, } @InProceedings{Kelly1991a, author = "J.D. Kelly Jr. and L. Davis.", booktitle = "{Proceedings of the 4th International Conference on Genetic Algorithms (ICGA91)}", title = "{Hybridizing the genetic algorithm and the k nearest neighbors classification algorithm}", pages = "377--383", year = "1991", month = jul, editor = "Lashon B. Booker and Richard K. Belew", publisher = "Morgan Kaufmann", ISBN = "1-55860-208-9", } @inproceedings{Cantu-Paz2002a, author = {Erick Cant\'{u}-Paz}, title = {Feature Subset Selection By Estimation Of Distribution Algorithms}, booktitle = {GECCO '02: Proceedings of the Genetic and Evolutionary Computation Conference}, year = {2002}, isbn = {1-55860-878-8}, pages = {303--310}, publisher = {Morgan Kaufmann}, } % distinguish between the filter and the wrapper approach to feature selection @InProceedings{John1994a, author = {G. John and R. Kohavi and K. Phleger}, title = {Irrelevant features and the feature subset problem.}, booktitle = {Proceedings of the 11th International Conference on Machine Learning}, pages = {121--129}, year = {1994}, publisher = {Morgan Kaufmann}, } @Article{Jain1997a, author = {A. Jain and D. Zongker}, title = {Feature selection: evaluation, application and small sample performance}, journal = {IEEE Trans. on Pattern Analysis and Machine Intelligence}, year = {1997}, volume = {19}, number = {2}, pages = {153--158}, } @InProceedings{Punch1993a, author = "W.F. Punch and E.D. Goodman and M. Pei and L. Chia-Shun and P. Hovland and R. Enbody", title = "{Further research on feature selection and classification using genetic algorithms}", pages = "557--564", booktitle = "{Proceedings of the 5th International Conference on Genetic Algorithms (ICGA93)}", editor = "Stephanie Forrest", publisher = "Morgan Kaufmann", ISBN = "1-55860-299-2", year = "1993", } @Article{Raymer2000a, author = {M.L. Raymer and W.F. Punch and E.D. Goodman and L.A. Kuhn and A.K. Jain}, title = {Dimensionality reduction using genetic algorithms}, journal = {IEEE Transactions on Evolutionary Computation}, year = {2000}, volume = {4}, number = {2}, pages = {164--171}, } % GP for feature selection (cited in 'A feild guide to GP 12.2' % W. B. Langdon and B. F. Buxton. Genetic programming for mining DNA chip data % from cancer patients. Genetic Programming and Evolvable Machines, 5(3):251–257, % September 2004. ISSN 1389-2576. URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ % ftp/papers/wbl_dnachip.pdf. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Genetic Programming %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % J. R. Koza. A genetic approach to the truck backer upper problem and the inter-twined spiral % problem. In Proceedings of IJCNN International Joint Conference on Neural Networks, % volume IV, pages 310–318. IEEE Press, 1992. @Book{Koza1992, author = {J.R. Koza}, title = {Genetic Programming: on the programming of computers by means of natural selection}, publisher = {MIT Press}, year = {1992}, } @Book{Koza1994, author = {J.R. Koza}, title = {Genetic Programming II}, publisher = {MIT Press}, year = {1994}, } @Book{Poli2008, author = {R. Poli and W.B. Langdon and N.F. McPhee}, title = {A field guide to genetic programming, freely available at http://www.gp-field-guide.org.uk}, publisher = {lulu.com}, year = {2008}, } @Article{Kushchu2002, author = {I. Kushchu}, title = {An evaluation of evolutionary generalization in genetic programming}, journal = {Artificial Intelligence Review}, year = {2002}, volume = {18}, number = {1}, pages = {3--14}, } @InProceedings{Gathercole1997, author = {C. Gathercole and P. Ross}, title = {Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness}, booktitle = {Genetic Programming 1997: Proc. Second Annual Conference}, pages = {119--127}, year = {1997}, editor = {J.R. Koza and K. Deb and M. Dorigo and D.B. Fogel and M. Garzon and H. Iba and R.L. Riolo}, publisher = {Morgan Kaufmann}, } @InCollection{Kretowski2005a, author = {Marek Kretowski and Marek Grzes}, title = {Global learning of decision trees by an evolutionary algorithm }, booktitle = {Information Processing and Security Systems}, pages = {401--410}, publisher = {Springer}, year = {2005}, editor = {}, volume = {3}, } @InProceedings{Koza1991a, author = {John Koza}, title = {Concept formation and decision tree induction using genetic programming paradigm}, booktitle = {Proc. Int. Conf. on Parallel Problem Solving from Nature}, pages = {124--128}, year = {1991}, volume = {496}, series = {LNCS}, publisher = {Springer}, } @article{Krawiec2002a, author = {K. Krawiec}, title = {Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks}, journal = {Genetic Programming and Evolvable Machines}, volume = {3}, number = {4}, year = {2002}, issn = {1389-2576}, pages = {329--343}, publisher = {Kluwer}, abstract = {In this paper we use genetic programming for changing the representation of the input data for machine learners. In particular, the topic of interest here is feature construction in the learning-from-examples paradigm, where new features are built based on the original set of attributes. The paper first introduces the general framework for GP-based feature construction. Then, an extended approach is proposed where the useful components of representation (features) are preserved during an evolutionary run, as opposed to the standard approach where valuable features are often lost during search. Finally, we present and discuss the results of an extensive computational experiment carried out on several reference data sets. The outcomes show that classifiers induced using the representation enriched by the GP-constructed features provide better accuracy of classification on the test set. In particular, the extended approach proposed in the paper proved to be able to outperform the standard approach on some benchmark problems on a statistically significant level.}, } @Article{Rivero2009a, author = {Daniel Rivero and Juli\'{a}n Dorado and Juan R. Rabu\~{n}al and Alejandro Pazos}, title = {Modifying genetic programming for artificial neural network development for data mining}, journal = {Soft Computing - A Fusion of Foundations, Methodologies and Applications}, year = {2009}, volume = {13}, number = {3}, pages = {291--305}, abstract = {The development of artificial neural networks (ANNs) is usually a slow process in which the human expert has to test several architectures until he finds the one that achieves best results to solve a certain problem. However, there are some tools that provide the ability of automatically developing ANNs, many of them using evolutionary computation (EC) tools. One of the main problems of these techniques is that ANNs have a very complex structure, which makes them very difficult to be represented and developed by these tools. This work presents a new technique that modifies genetic programming (GP) so as to correctly and efficiently work with graph structures in order to develop ANNs. This technique also allows the obtaining of simplified networks that solve the problem with a small group of neurons. In order to measure the performance of the system and to compare the results with other ANN development methods by means of evolutionary computation (EC) techniques, several tests were performed with problems based on some of the most used test databases in the Data Mining domain. These comparisons show that the system achieves good results that are not only comparable to those of the already existing techniques but, in most cases, improve them.}, } @InProceedings{Bot2000a, author = {M.C.J. Bot and W.B. Langdon}, title = {Application of genetic programming to induction of linear classification trees}, booktitle = {{Genetic Programming: Proceedings of the 3rd European Conference (EuroGP 2000)}}, pages = {247--258}, year = {2000}, volume = {1802}, series = {LNCS}, publisher = {Springer}, } @InProceedings{Folino2000a, author = {G. Folino and C. Pizzyti and G. Spezzano}, title = {Genetic programming and simulated annealing: a hybrid method to evolve decision trees}, booktitle = {{Genetic Programming: Proceedings of the 3rd European Conference (EuroGP 2000)}}, pages = {294--303}, year = {2000}, volume = {1802}, series = {LNCS}, publisher = {Springer}, } @InProceedings{Marmelstein1998a, author = {R.E. Marmelstein and G.B. Lamont}, title = {Pattern classification using a hybrid genetic algorithm -- decision tree approach}, booktitle = {{Genetic Programming 1998: Proceedings of the 3rd Annual Conference (GP'98)}}, pages = {223--231}, year = {1998}, publisher = {Morgan Kaufmann}, } @InProceedings{Gilbert1998a, author = {R.G. Gilbert and R. Goodacre and B. Shann and D.B. Kell and J. Taylor and J.J. Rowland}, title = {Genetic programming-based variable seletion for high-dimensional data}, booktitle = {{Genetic Programming 1998: Proc. 3rd Annual Conf. (GP'98)}}, pages = {109--115}, year = {1998}, publisher = {Morgan Kaufmann}, } @InProceedings{Hu1998a, author = {Y.-J. Hu}, title = {A genetic programming approach to constructive induction}, booktitle = {Genetic Programming 1998: Proceedings of the 3rd Annual Conference}, pages = {146--151}, year = {1998}, publisher = {Morgan Kaufmann}, } @InProceedings{Ishibuchi2000c, author = {H. Ishibuchi and T. Nakashima}, title = {Multi-objective pattern and feature selection by a genetic algorithm}, booktitle = {{Proceedings of the 2000 Genetic and Evolutionary Computation Conference (GECCO'2000)}}, pages = {1069--1076}, year = {2000}, publisher = {Morgan Kaufmann}, } @InProceedings{Folino2003a, author = {G. Folino and C. Pizzuti and G. Spezzano}, title = {Ensemble techniques for parallel genetic programming based classifiers}, booktitle = {{Proc. European Conf. on Genetic Programming (EuroGP'03)}}, pages = {59--69}, year = {2003}, } @InProceedings{Iba1999a, author = {H. Iba}, title = {Bagging, Boosting and bloating in genetic programming}, booktitle = {{Proc. of the Genetic and Evolutionary Computation Conference (GECCO'99)}}, pages = {1053--1060}, year = {1999}, } @Article{Song2005a, author = {D. Song and M.I. Heywood and A.N. Zincir-Heywood}, title = {Training genetic programming on half a million patterns: an example from anomaly detection}, journal = {IEEE Transactions on Evolutionary Computation}, year = {2005}, volume = {9}, number = {3}, pages = {225--239}, } @InProceedings{Keijzer2000a, author = {M. Keijzer and V. Babovic}, title = {Genetic programming, ensemble methods, and the bias/variance/tradeoff -- introductory investigation}, booktitle = {{Proc. of the European Conf. on Genetic Programming (EuroGP'00)}}, pages = {76--90}, year = {2000}, } @InProceedings{Paris2001a, author = {G. Paris and D. Robilliard and C. Fonlupt}, title = {Applying Boosting techniques to genetic programming}, booktitle = {Artificial Evolution 2001}, pages = {267--278}, year = {2001}, volume = {2310}, series = {LNCS}, publisher = {Springer}, } @PhdThesis{Schmidhuber1987a, author = {J\"{u}rgen Schmidhuber}, title = {Evolutionary principles in self-referential learning. (On learning how to learn: The meta-meta-... hook.)}, school = {Institut f. Informatik, Tech. Univ. Munich}, year = {1987}, } @InCollection{Burke2003a, author = {E.K. Burke and G. Kendall and J. Newall and E. Hart and P. Russ and S. Schulenburg}, editor = {F. Glover and G. Kochenberger}, title = {Hyper-heuristics: An Emerging Direction in Modern Search Technology}, booktitle = {Handbook of Meta-heuristics}, publisher = {Kluwer}, year = {2003}, pages = {457--474} } @InCollection{Burke2005a, author = {E.K. Burke and G. Kendall}, editor = {E.K. Burke and G. Kendall}, booktitle = {Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques}, title = {Introduction}, publisher = {Springer}, year = {2005}, pages = {5--18} } @InCollection{Burke2009a, title = {Exploring Hyper-heuristic Methodologies with Genetic Programming.}, author = {Edmund K. Burke and Mathew R. Hyde and Graham Kendall and Gabriela Ochoa and Ender Ozcan and John R. Woodward}, booktitle = {Collaborative Computational Intelligence}, editor = {C. Mumford and L. Jain}, publisher = {Springer}, year = {2009}, } @InProceedings{Woodward2003a, author = {John R. Woodward}, title = {{GA or GP? That is not the question}}, booktitle = {{Proceedings of the 2003 Congress on Evolutionary Computation CEC2003}}, year = {2003}, pages = {1056--1063}, publisher = {IEEE} } @Book{Pappa2010a, author = {Gisele L. Pappa and Alex A. Freitas}, title = {Automating the Design of Data Mining Algorithms. An Evolutionary Computation Approach}, publisher = {Springer}, series = {Natural Computing Series}, year = {2010}, } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Evolving ensembles %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 @Book{Kuncheva2004a, author = {Ludmila I. Kuncheva}, title = {Combining Pattern Classifiers: Methods and Algorithms}, publisher = {Wiley}, year = {2004}, } @InProceedings{Guerra-Salcedo1999a, author = {C. Guerra-Salcedo and D. Whitley}, title = "{Genetic approach to feature selection for ensemble creation}", pages = {236--243}, booktitle = "{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99)}", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", publisher = "Morgan Kaufmann", } @InProceedings{Jong2004a, author = {K. Jong and J. Marh and A. Cornuejols and E. Marchiori and M. Sebag}, title = {Ensemble feature ranking}, booktitle = {Proc. ECML-PKDD'04}, pages = {267--268}, year = {2004}, publisher = {IEEE}, abstract = {A crucial issue for Machine Learning and Data Mining is Feature Selection, selecting the relevant features in order to focus the learning search. A relaxed setting for Feature Selection is known as Feature Ranking, ranking the features with respect to their relevance. This paper proposes an ensemble approach for Feature Ranking, aggregating feature rankings extracted along independent runs of an evolutionary learning algorithm named ROGER. The convergence of ensemble feature ranking is studied in a theoretical perspective, and a statistical model is devised for the empirical validation, inspired from the complexity framework proposed in the Constraint Satisfaction domain. Comparative experiments demonstrate the robustness of the approach for learning (a limited kind of) non-linear concepts, specifically when the features significantly outnumber the examples.}, } @article{Opitz1996a, title={{Generating Accurate and Diverse Members of a Neural-network Ensemble}}, author={Opitz, D.W. and Shavlik, J.W.}, journal={Advances in Neural Information Processing Systems}, pages={535--541}, year={1996}, publisher={Morgan Kaufmann} } @Article{Chandra2006a, author = {Arjun Chandra and Xin Yao}, title = {Ensemble Learning Using Multi-Objective Evolutionary Algorithms}, journal = {Journal of Mathematical Modelling and Algorithms}, publisher= {Springer}, year = {2006}, volume = {5}, number = {4}, pages = {417--445}, note = {Introduces DIVACE}, } @Article{Chandra2006b, author = {Arjun Chandra and Xin Yao}, title = {Evolving Hybrid Ensembles of Learning Machines for Better Generalisation}, journal = {Neurocomputing}, year = {2006}, volume = {69}, number = {7--9}, pages = {686--700}, note = {Introduces DIVACE-II}, } @InCollection{Kim2005a, author = {Kyung-Joong Kim and Ji-Oh Yoo and Sung-Bae Cho}, title = {Robust Inference of Bayesian Networks Using Speciated Evolution and Ensemble}, booktitle = {Foundations of Intelligent Systems}, pages = {92--101}, publisher = {Springer}, year = {2005}, volume = {3488/2005}, series = {LNCS}, } @article{Folino2006a, title={{GP ensembles for large-scale data classification}}, author={Folino, G. and Pizzuti, C. and Spezzano, G.}, journal={IEEE Transactions on Evolutionary Computation}, volume={10}, number={5}, pages={604--616}, year={2006} } @InProceedings{Cho2004a, author = {Sung-Bae Cho and Chanho Park}, title = {{Speciated GA for optimal ensemble classifiers in DNA microarray classification}}, booktitle = {{Congress on Evolutionary Computation (CEC 2004)}}, pages = {590--597}, year = {2004}, volume = {1}, } @InProceedings{Thompson1998a, author = {S. Thompson}, title = {Pruning boosted classifiers with a real valued genetic algorithm}, booktitle = {{Research and Development in Expert Systems XV -- Proceedings of ES'98}}, pages = {133-146}, year = {1998}, publisher = {Springer}, } @InProceedings{Thompson1999a, author = {S. Thompson}, title = {Genetic algorithms as postprocessors for data mining}, booktitle = {{Data Mining with Evolutionary Algorithms: Research Directions -- Papers from the AAAI Workshop. Tech report WS--99--06}}, pages = {18--22}, year = {1999}, publisher = {AAAI Press}, } @InProceedings{Jin2004a, author = {Y. Jin and B. Sendhoff}, title = {Reducing fitness evaluations using clustering techniques and neural network ensembles}, booktitle = {{Genetic and Evolutionary Computation Conference (GECCO--2004)}}, pages = {688--699}, year = {2004}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = {3102}, isbn = {3-540-22344-4}, } @Article{Abbass2003a, author = {H.A. Abbass}, title = {Speeding up backpropagation using multiobjective evolutionary algorithms}, journal = {Neural Computation}, year = {2003}, volume = {15}, number = {11}, pages = {2705--2726}, } @inproceedings{Gagne2007a, author = {Christian Gagn\'{e} and Mich\`{e}le Sebag and Marc Schoenauer and Marco Tomassini}, title = {Ensemble learning for free with evolutionary algorithms?}, booktitle = {{GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation}}, year = {2007}, isbn = {978-1-59593-697-4}, pages = {1782--1789}, publisher = {ACM}, } @Article{Cho1999a, author = {S.-B. Cho}, title = {Pattern recognition with neural networks combined by genetic algorithm}, journal = {Fuzzy Sets and Systems}, year = {1999}, volume = {103}, pages = {339--347}, note = {See Kuncheva2004a p.167} } @Article{Lam1995a, author = {L. Lam and C.Y. Suen}, title = {Optimal combination of pattern classifiers}, journal = {Pattern Recognition Letters}, year = {1995}, volume = {16}, pages = {945--954}, note = {See Kuncheva2004a p.167} } @InProceedings{Ruta2001a, author = {D. Ruta and B. Gabrys}, title = {Application of the evolutionary algorithms for classifier selection in multiple classifier systems with majority voting}, booktitle = {Proc. 2nd International Workshop on Multiple Classifier Systems}, pages = {399--408}, year = {2001}, editor = {J. Kittler and F. Roli}, volume = {2096}, series = {LNCS}, publisher = {Springer--Verlag}, note = {See Kuncheva2004a p.321} } @InProceedings{Sirlantzis2001a, author = {K. Sirlantzis and M.C. Fairhurst and M.S. Hoque}, title = {Genetic algorithms for multi-classifier system configuration: a case study in character recognition}, booktitle = {Proc. 2nd International Workshop on Multiple Classifier Systems}, pages = {99--108}, year = {2001}, editor = {J. Kittler and F. Roli}, volume = {2096}, series = {LNCS}, publisher = {Springer--Verlag}, note = {See Kuncheva2004a p.321} } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Evolving Cellular Automata %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 @PhdThesis{Ganguly2004a, author = {N. Ganguly}, title = {Cellular Automata Evolution: Theory and Applications in Pattern Classification and Recognition}, school = {Bengal Engineering College}, year = {2004}, } @InProceedings{Maji2004a, author = {P. Maji and B.K. Sikdar and P.P. Chaudhuri}, title = {Cellular automata evolution for distributed data mining}, booktitle = {Cellular Automata}, pages = {40--49}, year = {2004}, volume = {3305}, series = {LNCS}, } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 % Misc %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 @PhdThesis{McCallumThesis, author = {Andrew McCallum}, title = {Reinforcement Learning with Selective Perception and Hidden State}, school = {University of Rochester}, year = {1996}, } @TechReport{Michalski1986a, author = {R.S. Michalski and I. Mozetic and J. Hong and N. Lavrac}, title = {The {AQ15} inductive learning system: an overview and experiments}, institution = {University of Illinois}, year = {1986}, number = {UIUCDCS-R-86-1260}, } @Article{Rivest1987a, author = {R.L. Rivest}, title = {Learning decision lists}, journal = {Machine Learning}, year = {1987}, volume = {2}, number = {3}, pages = {229--246}, } @Article{Furnkranz1998a, author = {J. F\"{u}rnkranz}, title = {Integrative windowing}, journal = {Journal of Artificial Intelligence Research}, year = {1998}, volume = {8}, pages = {129--164}, } % Shows that finding the best oblique decision tree is NP-complete. % Used simulated annealing to perturbe hyperplanes in trees, which was slow. % See Cantu-Paz2003a sect. 2 for a brief review. @InProceedings{Heath1993a, author = {D. Heath and S. Kasif and S. Salzberg}, title = {Induction of oblique decision trees}, booktitle = {Proc. 13th Int. Conf. on Art. Int.}, pages = {1002-1007}, year = {1993}, publisher = {Morgan Kaufmann}, } @InProceedings{Heidrich-Meisner2008a, author = {Verena Heidrich-Meisner and Christian Igel}, title = {Evolution Strategies for Direct Policy Search}, booktitle = {{Proc. Int. Conf. on Parallel Problem Solving from Nature PPSN X}}, pages = {428--437}, year = {2008}, editor = {G. Rudolph et al.}, volume = {5199}, series = {LNCS}, publisher = {Springer--Verlag}, } @Article{Peters2008a, author = {J. Peters and S. Schaal}, title = {Natural actor-critic}, journal = {Neurocomputing}, year = {2008}, volume = {71}, number = {7--9}, pages = {1180--1190}, } @InProceedings{Tamaddoni-Nezhad2000a, author = {A. Tamaddoni-Nezhad and S.H. Muggleton}, title = {Searching the subsumption lattice by a genetic algorithm}, booktitle = {Proceedings of the 10th International Conference on Inductive Logic Programming}, pages = {243--252}, year = {2000}, editor = {J. Cussens and A. Frisch}, publisher = {Springer-Verlag}, } @InCollection{Tamaddoni-Nezhad2003a, author = {Alireza Tamaddoni-Nezhad and Stephen Muggleton}, title = {{A Genetic Algorithms Approach to ILP}}, booktitle = {Inductive Logic Programming}, pages = {285--300}, publisher = {Springer}, year = {2003}, volume = {2583/2003}, series = {LNCS}, } @InProceedings{Divina2002b, author = {F. Divina and M. Keijzer and E. Marchiori}, title = {Non-universal suffrage selection operators favor population diversity in genetic algorithms}, booktitle = {Benelearn 2002: Proceedings of the 12th Belgian-Dutch Conference on Machine Learning (Technical report UU-CS-2002-046)}, pages = {23--30}, year = {2002}, } @Article{Srinivas1994a, author = {N. Srinivas and K. Deb}, title = {Multi-objective function optimization using non-dominated sorting genetic algorithm}, journal = {Evolutionary Computation}, year = {1994}, volume = {2}, number = {3}, pages = {221--248}, } @InProceedings{Storn1996a, author = {R. Storn and K. Price}, title = {Minimizing the real functions of the ICEC'96 contest by differential evolution}, booktitle = {Proc. of the IEEE Int. Conf. on Evolutionary Computation}, pages = {842--844}, year = {1996}, publisher = {IEEE}, } @Book{Reeves2002a, author = {C.R. Reeves and J.E. Rowe}, title = {Genetic Algorithms -- Principles and Perspectives. A Guide to GA Theory}, publisher = {Kluwer}, year = {2002}, ISBN = {1402072406}, } @misc{UCIRepository, author = "A. Asuncion and D.J. Newman", year = "2009", title = "{UCI} Machine Learning Repository http://www.ics.uci.edu/$\sim$mlearn/{MLR}epository.html", institution = "University of California, Irvine, School of Information and Computer Sciences" } @misc{GPBibliography, author = "William Langdon and Steven Gustafson and John Koza. ", year = "2009", title = "The Genetic Programming Bibliography http://www.cs.bham.ac.uk/~wbl/biblio/", } @Article{Vilalta2002a, author = {R. Vilalta and Y. Drissi}, title = {A perspective view and survey of meta-learning}, journal = {Artificial Intelligence Review}, year = {2002}, volume = {18}, number = {2}, pages = {77--95}, } @InCollection{Giraud-Carrier2002a, author = {C. Giraud-Carrier and J. Keller}, editor = {J. Meij}, booktitle = {Dealing with the data flood}, title = {Meta-Learning}, publisher = {STT/Beweton}, year = {2002}, } @Article{Wolpert1996a, author = {David H. Wolpert}, title = {The lack of a priori distinctions between learning algorithms}, journal = {Neural Computation}, year = {1996}, volume = {8}, number = {7}, pages = {1341--1390}, } % introduced the Widrow-Hoff delta rule @InProceedings{Widrow1960a, author = {B. Widrow and M.E. Hoff}, title = {Adaptive switching circuits}, booktitle = {1960 IRE WESTCON Convention Rec.}, pages = {96--104}, year = {1960}, } @Article{Bacardit2009b, author = {J. Bacardit and M. Stout and J.D. Hirst and A. Valencia and R.E. Smith and N. Krasnogor}, title = {Automated alphabet reduction for protein datasets}, journal = {BMC Bioinformatics}, year = {2009}, volume = {10}, number = {6}, } @Article{Stout2008a, author = {M. Stout and J. Bacardit and J.D. Hirst and N. Krasnogor}, title = {Prediction of recursive convex hull class assignment for protein residues}, journal = {Bioinformatics}, year = {2008}, volume = {24}, number = {7}, pages = {916--923}, }