Evoluzione genetica applicata agli alberi di classificazione
11. Bibliografia

Fu, Zhiwei, Golden, Bruce L, Lele, Shreevardhan, Raghavan, S, Wasil, Edward A: A genetic algorithm-based approach for building accurate decision trees, INFORMS Journal on Computing15(1), INFORMS, 3–22, 2003

Kennedy, HC, Chinniah, C, Bradbeer, P, Morss, L: The contruction and evaluation of decision trees: A comparison of evolutionary and concept learning methods, Evolutionary Computing, Springer, 147–161, 1997

Cormen, Leisersonet al.: Rivest, Introduction to algorithms, MIT press, 1990

Michalewicz, Zbigniew: Genetic algorithms+ data structures= evolution programs, springer, 1996

Quinlan, Ross: C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, 1993

Hall, Mark, Frank, Eibe, Holmes, Geoffrey, Pfahringer, Bernhard, Reutemann, Peter, Witten, Ian H: The WEKA data mining software: an update, ACM SIGKDD Explorations Newsletter 11(1), ACM, 10–18, 2009

Poli, Riccardo, Langdon, W William B, McPhee, Nicholas F, Koza, John R: A field guide to genetic programming, Lulu. com, 2008

Whitley, Darrell: The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials Is Best, Proceedings of the Third International Conference on Genetic Algorithms, San Francisco, CA: Morgan Kaufmann, 116–121, Eds: Schaffer, David J., 1989

Crepeau, Ronald L.: Genetic Evolution of Machine Language Software, Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, 121–134, Eds: Rosca, Justinian P., 9 July 1995
Abstract: Genetic Programming (GP) has a proven capability to routinely evolve software that provides a solution function for the specified problem. Prior work in this area has been based upon the use of relatively small sets of pre-defined operators and terminals germane to the problem domain. This paper reports on GP experiments involving a large set of general purpose operators and terminals. Specifically, a microprocessor architecture with 660 instructions and 255 bytes of memory provides the operators and terminals for a GP environment. Using this environment, GP is applied to the beginning programmer problem of generating a desired string output, e.g., "Hello World". Results are presented on: the feasibility of using this large operator set and architectural representation; and, the computations required to breed string outputting programs vs. the size of the string and the GP parameters employed.

Poli, Riccardo, Langdon, W. B.: A New Schema Theory for Genetic Programming with One-Point Crossover and Point Mutation, School of Computer Science No. CSRP-97-3, January 1997, Presented at GP-97
Abstract: In this paper we first review the main results obtained in the theory of schemata in Genetic Programming (GP) emphasising their strengths and weaknesses. Then we propose a new, simpler definition of the concept of schema for GP which is quite close to the original concept of schema in genetic algorithms (GAs). Along with a new form of crossover, one-point crossover, and point mutation this concept of schema has been used to derive an improved schema theorem for GP which describes the propagation of schemata from one generation to the next. In the paper we discuss this result and show that our schema theorem is the natural counterpart for GP of the schema theorem for GAs, to which it asymptotically converges.

Holland, John H.: Adaptation in Natural and Artificial Systems, University of Michigan Press, 1975

Kotsiantis, S B: Supervised Machine Learning: A Review of Classification Techniques, Informatica 31(3), 249–268, 2007
Abstract: Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.

Barros, Rodrigo Coelho, Basgalupp, Márcio Porto, de Carvalho, André Carlos Ponce Leon Ferreira, Freitas, Alex Alves: A Survey of Evolutionary Algorithms for Decision-Tree Induction., IEEE Transactions on Systems, Man, and Cybernetics, Part C 42(3), 291–312, 2012

Breiman, L., Friedman, J, Olshen, R., Stone, C.: Classification and Regression Trees, Pacific Grove, 1984

Seni, Giovanni, IV, John F. Elder: Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions, Ensemble Methods in Data Mining, Morgan & Claypool Publishers, 2010

Kohavi, Ron, Quinlan, Ross: Decision Tree Discovery, IN HANDBOOK OF DATA MINING AND KNOWLEDGE DISCOVERY, University Press, 267–276, 1999

Alpaydin, Ethem: Introduction to Machine Learning, The MIT Press, 2004

Hornby, Gregory S, Globus, Al, Linden, Derek S, Lohn, Jason D: Automated antenna design with evolutionary algorithms, AIAA Space, 19–21, 2006

Spector, Lee: Automatic Quantum Computer Programming: a genetic programming approach, volume 7, Springer, 2004

Kononenko, Igor: A counter example to the stronger version of the binary tree hypothesis, ECML-95 workshop on Statistics, machine learning, and knowledge discovery in databases, 31, 1995

Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome, Franklin, James: The elements of statistical learning: data mining, inference and prediction, The Mathematical Intelligencer 27(2), Springer, 83–85, 2005

Langdon, W. B.: Size Fair and Homologous Tree Genetic Programming Crossovers, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, Morgan Kaufmann, 1092–1097, Eds: Banzhaf, Wolfgang, Daida, Jason, Eiben, Agoston E., Garzon, Max H., Honavar, Vasant, Jakiela, Mark, Smith, Robert E., 13-17July 1999
Abstract: Size fair and homologous crossover genetic operators for tree based genetic programming are described and tested. Both produce considerably reduced increases in program size and no detrimental effect on GP performance. GP search spaces are partitioned by the ridge in the number of program versus their size and depth. A ramped uniform random initialisation is described which straddles the ridge. With subtree crossover trees increase about one level per generation leading to sub-quadratic bloat in length.

Llora, Xavier, Garrell, Josep M: Evolution of decision trees, Forth Catalan Conference on Artificial Intelligence (CCIA’2001), 115–122, 2001

Bishop, Christopher M et al.: Pattern recognition and machine learning, volume 1, springer New York, 2006

Bache, K., Lichman, M.: UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences, 2013

Holland, John H: Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence., U Michigan Press, 1975

Koza, John R: Genetic programming: on the programming of computers by means of natural selection, volume 1, MIT press, 1992