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A survey of semantic methods in genetic programming

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Abstract

Several methods to incorporate semantic awareness in genetic programming have been proposed in the last few years. These methods cover fundamental parts of the evolutionary process: from the population initialization, through different ways of modifying or extending the existing genetic operators, to formal methods, until the definition of completely new genetic operators. The objectives are also distinct: from the maintenance of semantic diversity to the study of semantic locality; from the use of semantics for constructing solutions which obey certain constraints to the exploitation of the geometry of the semantic topological space aimed at defining easy-to-search fitness landscapes. All these approaches have shown, in different ways and amounts, that incorporating semantic awareness may help improving the power of genetic programming. This survey analyzes and discusses the state of the art in the field, organizing the existing methods into different categories. It restricts itself to studies where semantics is intended as the set of output values of a program on the training data, a definition that is common to a rather large set of recent contributions. It does not discuss methods for incorporating semantic information into grammar-based genetic programming or approaches based on formal methods. The objective is keeping the community updated on this interesting research track, hoping to motivate new and stimulating contributions.

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Notes

  1. Consistently with [70], here we use the term “effective code” as opposite to “redundant code”. Quoting [70], we define the term “effective code” as the part of the genotype of an individual that “makes a difference in the result of the individual’s fitness calculation for at least one of the fitness cases”. A slightly different, although equivalent, definition of the term “effective code” can be found in [5]: the part of the code that needs to be executed in order to determine the fitness of an individual.

References

  1. L.C.J. Beadle, Semantic and Structural Analysis of Genetic Programming. PhD thesis, (University of Kent, Canterbury, 2009)

  2. L.C.J. Beadle, C.G. Johnson, Semantically driven crossover in genetic programming, in Proceedings of the IEEE World Congress on Computational Intelligence (IEEE Press, Hong Kong, 2008), pp. 111–116

  3. L.C.J. Beadle, C.G. Johnson, Semantic analysis of program initialisation in genetic programming. Genet. Program. Evol. Mach. 10(3), 307–337 (2009)

    Article  Google Scholar 

  4. L.C.J. Beadle, C.G. Johnson, Semantically driven mutation in genetic programming, in 2009 IEEE Congress on Evolutionary Computation, ed. by A. Tyrrell (IEEE Computational Intelligence Society, IEEE Press, Trondheim, 2009), pp. 1336–1342

  5. M. Brameier, W. Banzhaf, Linear Genetic Programming, Number XVI in Genetic and Evolutionary Computation. (Springer, Berlin, 2007)

    Google Scholar 

  6. R.E. Bryant, Graph-based algorithms for boolean function manipulation. IEEE Trans. Comput. 35, 677–691 (1986)

    Article  MATH  Google Scholar 

  7. E. Burke, S. Gustafson, G. Kendall, N. Krasnogor, Advanced population diversity measures in genetic programming, in Parallel Problem Solving from Nature—PPSN VII, Number 2439 in Lecture Notes in Computer Science, LNCS, ed. by J.J. Merelo-Guervos, et al. (Springer, Granada, 2002), pp. 341–350

  8. E.K. Burke, S. Gustafson, G. Kendall, Diversity in genetic programming: an analysis of measures and correlation with fitness. IEEE Trans. Evol. Comput. 8(1), 47–62 (2004)

    Article  Google Scholar 

  9. M. Castelli, S. Silva, L. Vanneschi, A. Cabral, M.J. Vasconcelos, L. Catarino, J.A.M.B. Carreiras, Land cover/land use multiclass classification using gp with geometric semantic operators, in Proceedings of the 16th European Conference on Applications of Evolutionary Computation, EvoApplications’13 (Springer, Berlin, 2013), pp. 334–343

  10. M. Castelli, L. Vanneschi, S. Silva, Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators. Expert Syst. Appl. 40(17), 6856–6862 (2013)

    Article  Google Scholar 

  11. M. Castelli, L. Vanneschi, S. Silva, Semantic search-based genetic programming and the effect of intron deletion. IEEE Trans. Cybern. 2013. doi:10.1109/TSMCC.2013.2247754, ISSN: 2168-2267. In press

  12. R. Cleary, M. O’Neill, An attribute grammar decoder for the 01 multiconstrained knapsack problem, in Evolutionary Computation in Combinatorial Optimization—EvoCOP 2005, Volume 3448 of LNCS, ed. by G.R. Raidl, J. Gottlieb (Springer, Lausanne, 2005), pp. 34–45

  13. J.M. Daida, H. Li, R. Tang, A.M. Hilss, What makes a problem GP-hard? validating a hypothesis of structural causes, in Genetic and Evolutionary Computation—GECCO-2003, Volume 2724 of LNCS, ed. by E. Cantú-Paz, et al. (Springer, Chicago, 2003), pp. 1665–1677

  14. J.M. Daida, D.J. Ward, A.M. Hilss, S.L. Long, M.R. Hodges, J.T. Kriesel, Visualizing the loss of diversity in genetic programming, in Proceedings of the 2004 IEEE Congress on Evolutionary Computation (IEEE Press, Portland, 2004), pp. 1225–1232

  15. E.D. de Jong, R.A. Watson, J.B. Pollack, Reducing Bloat and Promoting Diversity Using Multi-Objective Methods. (Morgan Kaufmann, San Francisco, CA, 2001), pp. 11–18

  16. P. D’haeseleer, J. Bluming, Effects of locality in individual and population evolution, in Advances in Genetic Programming, Chapter 8, ed. by K.E. Kinnear, Jr. (MIT Press, Cambridge, MA, 1994) pp. 177–198.

    Google Scholar 

  17. S. Dignum, R. Poli, Operator equalisation and bloat free GP, in Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008, Volume 4971 of Lecture Notes in Computer Science, ed. by M. O’Neill, et al. (Springer, Naples, 2008), pp. 110–121

  18. A. Ekart, S. Z. Nemeth, A metric for genetic programs and fitness sharing, in Genetic Programming, Proceedings of EuroGP’2000, Volume 1802 of LNCS (Edinburgh, 2000), pp. 259–270

  19. E. Galvan, L. Trujillo, J. McDermott, A. Kattan, Locality in continuous fitness-valued cases and genetic programming difficulty, in EVOLVE—A Bridge Between Probability, Set Oriented Numerics, and Evolutionary Computation II, Volume 175 of Advances in Intelligent Systems and Computing (Springer, Mexico City, 2012), pp. 41–56

  20. E. Galván-López, J. Mcdermott, M. O’Neill, A. Brabazon, Defining locality as a problem difficulty measure in genetic programming. Genet. Program. Evol. Mach. 12(4), 365–401 (2011)

    Article  Google Scholar 

  21. E. Galvan-Lopez, M. O’Neill, A. Brabazon, Towards understanding the effects of locality in GP, in Eighth Mexican International Conference on Artificial Intelligence (MICAI 2009, 2009), pp. 9–14

  22. M.A. Ghodrat, T. Givargis, A. Nicolau, Equivalence checking of arithmetic expressions using fast evaluation, in Proceedings of the 2005 International Conference on Compilers, Architectures and Synthesis for Embedded Systems, CASES ’05 (ACM, New York, NY, 2005), pp. 147–156

  23. D.E. Goldberg, U.-M. O’Reilly, Where does the good stuff go, and why? How contextual semantics influence program structure in simple genetic programming, in Proceedings of the First European Workshop on Genetic Programming, Volume 1391 of LNCS, W. Banzhaf et al. (Springer, Paris, 1998), pp. 16–36

  24. J. Gottlieb, G. R. Raidl, The effects of locality on the dynamics of decoder-based evolutionary search, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’00), ed. by L.D. Whitley, et al. (Morgan Kaufmann, Las Vegas, 2000), pp. 283–290

  25. S. Gustafson, An Analysis of Diversity in Genetic Programming. PhD thesis. (School of Computer Science and Information Technology, University of Nottingham, Nottingham, 2004)

  26. S. Gustafson, E.K. Burke, G. Kendall, Sampling of unique structures and behaviours in genetic programming, in Genetic Programming, Volume 3003 of Lecture Notes in Computer Science, ed. by M. Keijzer, et al. (Springer, Heidelberg, 2004), pp. 279–288

  27. S. Gustafson, E. K. Burke, N. Krasnogor, The tree-string problem: an artificial domain for structure and content search, in Proceedings of the 8th European Conference on Genetic Programming, Volume 3447 of Lecture Notes in Computer Science, ed. by M. Keijzer, et al. (Springer, Lausanne, 2005), pp. 215–226

  28. S. Gustafson, L. Vanneschi, Operator-based distance for genetic programming: Subtree crossover distance, in Proceedings of the 8th European Conference on Genetic Programming, Volume 3447 of Lecture Notes in Computer Science, ed. by M. Keijzer, et al. (Springer, Lausanne, 2005), pp. 178–189

  29. D. Jackson, Phenotypic diversity in initial genetic programming populations, in Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010, Volume 6021 of LNCS, ed. by A.I. Esparcia-Alcazar, et al. (Springer, Istanbul, 2010), pp. 98–109

  30. D. Jackson, Promoting phenotypic diversity in genetic programming, in PPSN 2010 11th International Conference on Parallel Problem Solving From Nature, Volume 6239 of Lecture Notes in Computer Science, ed. by R. Schaefer, et al. (Springer, Krakow, 2010), pp. 472–481

  31. C.G. Johnson, Deriving genetic programming fitness properties by static analysis, in Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002, Volume 2278 of LNCS, ed. by J.A. Foster, et al. (Springer, Kinsale, 2002), pp. 298–307

  32. C.G. Johnson, Genetic programming with guaranteed constraints, in Proceedings of the 4th International Conference on Recent Advances in Soft Computing, ed. by A. Lotfi, et al. (The Nottingham Trent University, Nottingham, 2002), pp. 134–140

  33. C.G. Johnson, What can automatic programming learn from theoretical computer science?, in The 2002 U.K. Workshop on Computational Intelligence (UKCI’02), ed. by X. Yao (Birmingham, 2002)

  34. W. Kantschik, W. Banzhaf, Linear-tree GP and its comparison with other GP structures, in Genetic Programming, Proceedings of EuroGP’2001, Volume 2038 of LNCS (Springer, Lake Como, 2001), pp. 302–312

  35. J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. (MIT Press, Cambridge, MA, 1992)

    MATH  Google Scholar 

  36. K. Krawiec, Medial crossovers for genetic programming, in Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012, Volume 7244 of LNCS (Springer, Malaga, 2012), pp. 61–72

  37. K. Krawiec, P. Lichocki et al., Approximating geometric crossover in semantic space, in GECCO ’09, ed. by G. Raidl (ACM, New York, 2009) pp. 987–994.

    Chapter  Google Scholar 

  38. K. Krawiec, T. Pawlak, Locally geometric semantic crossover: a study on the roles of semantics and homology in recombination operators. Genet. Program. Evol. Mach. 14(1), 31–63 (2013)

    Article  Google Scholar 

  39. W. Langdon, R. Poli, Foundations of Genetic Programming. (Springer, Berlin, 2002)

    Book  MATH  Google Scholar 

  40. M. Looks, On the behavioral diversity of random programs, in GECCO ’07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, vol. 2, ed. by D. Thierens, et al. (ACM Press, London, 2007), pp. 1636–1642

  41. A. Mambrini, L. Manzoni, A. Moraglio, Theory-laden design of mutation-based geometric semantic genetic programming for learning classification trees, in IEEE Congress on Evolutionary Computation, (IEEE, 2013), pp. 416–423

  42. J. McDermott, U.-M. O’Reilly, L. Vanneschi, K. Veeramachaneni, How far is it from here to there? A distance that is coherent with GP operators, in Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011, Volume 6621 of LNCS, ed. by S. Silva, et al. (Springer, Turin, 2011), pp. 190–202

  43. R.I. McKay, X.H. Nguyen, P.A. Whigham, Y. Shan, M. O’Neill. Grammar-based genetic programming: a survey. Genet. Program. Evol. Mach. 11(3/4), 365–396 (2010). Tenth Anniversary Issue: Progress in Genetic Programming and Evolvable Machines

    Google Scholar 

  44. N.F. McPhee, N.J. Hopper, Analysis of genetic diversity through population history, in Proceedings of the Genetic and Evolutionary Computation Conference, vol. 2, ed. by W. Banzhaf, et al. (Morgan Kaufmann, Orlando, FL, 1999), pp. 1112–1120

  45. N.F. McPhee, B. Ohs, T. Hutchison, Semantic building blocks in genetic programming, in Proceedings of the 11th European Conference on Genetic Programming, EuroGP’08 (Springer, Berlin, 2008), pp. 134–145

  46. J. Miller, P. Thomson, Cartesian genetic programming, in Genetic Programming, Proceedings of EuroGP’2000, Volume 1802 of LNCS (Springer, Edinburgh, 2000), pp. 121–132

  47. T.M. Mitchell, Machine Learning. (McGraw-Hill, New York, 1997)

    MATH  Google Scholar 

  48. D. Montana, Strongly typed genetic programming. Evol. Comput. 3(2), 199–230 (1995)

    Article  Google Scholar 

  49. A. Moraglio, K. Krawiec, C.G. Johnson et al., Geometric semantic genetic programming, in Parallel Problem Solving from Nature, PPSN XII (Part 1), Volume 7491 of Lecture Notes in Computer Science, ed. by C.A. Coello Coello (Springer, Berlin, 2012), pp. 21–31.

    Google Scholar 

  50. A. Moraglio, A. Mambrini, Runtime analysis of mutation-based geometric semantic genetic programming for basis functions regression, in GECCO, ed. by C. Blum, E. Alba (ACM, New York, 2013), pp. 989–996.

    Google Scholar 

  51. A. Moraglio, R. Poli, Topological interpretation of crossover, in Genetic and Evolutionary Computation—GECCO-2004, Part I, Volume 3102 of Lecture Notes in Computer Science, ed. by K. Deb, et al. (Springer, Seattle, WA, 2004), pp. 1377–1388

  52. M. Naoki, B. Mckay, X. H. Nguyen, E. Daryl, S. Takeuchi, A new method for simplifying algebraic expressions in genetic programming called equivalent decision simplification, in Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, IWANN ’09 (Springer, Berlin, 2009), pp. 171–178

  53. Q.U. Nguyen, Examining Semantic Diversity and Semantic Locality of Operators in Genetic Programming. PhD thesis.. (University College Dublin, 2011)

  54. Q.U. Nguyen, B. McKay, M. O’Neill, X.H. Nguyen, Self-adapting semantic sensitivities for semantic similarity based crossover, in Evolutionary Computation (CEC), 2010 IEEE Congress on (2010), pp. 1–7

  55. Q.U. Nguyen, E. Murphy, M. O’Neill, X.H. Nguyen, Semantic-based subtree crossover applied to dynamic problems, in Knowledge and Systems Engineering (KSE), 2011 Third International Conference on (2011), pp. 78–84

  56. Q.U. Nguyen, H.T. Nguyen, X.H. Nguyen, M. O’Neill et al., Improving the generalisation ability of genetic programming with semantic similarity based crossover, in Genetic Programming, Volume 6021 of Lecture Notes in Computer Science, ed. by A. Esparcia-Alcazar (Springer, Berlin, 2010) pp. 184–195.

    Google Scholar 

  57. Q.U. Nguyen, X.H. Nguyen, M. O’Neill, Semantic aware crossover for genetic programming: the case for real-valued function regression, in Proceedings of the 12th European Conference on Genetic Programming, EuroGP ’09, (Springer, Berlin, 2009), pp. 292–302

  58. Q.U. Nguyen, X.H. Nguyen, M. O’Neill, Semantics based mutation in genetic programming: The case for real-valued symbolic regression, in 15th International Conference on Soft Computing, Mendel’09, ed. by R. Matousek, L. Nolle (Brno, 2009), pp. 73–91

  59. Q.U. Nguyen, X.H. Nguyen, M. O’Neill, Examining the landscape of semantic similarity based mutation, in Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO ’11 (ACM, New York, NY, 2011), pp. 1363–1370

  60. Q.U. Nguyen, X.H. Nguyen, M. O’Neill, A. Agapitos, An investigation of fitness sharing with semantic and syntactic distance metrics, in Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012, Volume 7244 of LNCS, ed. by A. Moraglio, et al. (Springer, Malaga, 2012), pp. 109–120

  61. Q.U. Nguyen, X.H. Nguyen, M. O’Neill, B. McKay, The role of syntactic and semantic locality of crossover in genetic programming, in PPSN 2010 11th International Conference on Parallel Problem Solving From Nature, Volume 6239 of Lecture Notes in Computer Science (Springer, Krakow, 2010), pp. 533–542,

  62. Q.U. Nguyen, X.H. Nguyen, M. O’Neill, B. McKay, Semantics based crossover for boolean problems, in Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO ’10 (ACM, New York, NY, 2010), pp. 869–876

  63. Q.U. Nguyen, X.H. Nguyen, M. O’Neill, B. McKay, E. Galvan-Lopez, An analysis of semantic aware crossover, in Proceedings of the International Symposium on Intelligent Computation and Applications, Volume 51 of Communications in Computer and Information Science, ed. by Z. Cai, et al. (Springer, Berlin, 2009), pp. 56–65

  64. Q.U. Nguyen, X.H. Nguyen, M. O’Neill, R. McKay, D.N. Phong, On the roles of semantic locality of crossover in genetic programming. Inf. Sci. 235(0), 195–213 (2013)

    MATH  Google Scholar 

  65. Q.U. Nguyen, X.H. Nguyen, M. O’Neill, R.I. McKay, E. Galvan-Lopez, Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genet. Program. Evol. Mach. 12(2), 91–119 (2011)

    Article  Google Scholar 

  66. Q.U. Nguyen, M. O’Neill, X.H. Nguyen, Predicting the tide with genetic programming and semantic-based crossovers, in Knowledge and Systems Engineering (KSE), 2010 Second International Conference on (2010), pp. 89–95

  67. Q.U. Nguyen, M. O’Neill, X.H. Nguyen, B. McKay, E.G. Lopez, Semantic similarity based crossover in GP: the case for real-valued function regression, in 9th International Conference, Evolution Artificielle, EA 2009, Volume 5975 of Lecture Notes in Computer Science, ed. by P. Collet, et al. (Springer, Strasbourg, 2009), pp. 170–181 Revised Selected Papers

  68. T.H. Nguyen, X.H. Nguyen, A brief overview of population diversity measures in genetic programming, in Proceedings of the Third Asian-Pacific Workshop on Genetic Programming, ed. by T.L. Pham, et al. (Military Technical Academy, Hanoi, 2006), pp. 128–139

  69. X.H. Nguyen, R.I. McKay, D. Essam, Representation and structural difficulty in genetic programming. IEEE Trans. Evol. Comput. 10(2), 157–166 (2006)

    Article  Google Scholar 

  70. P. Nordin, F. Francone, W. Banzhaf, Explicitly defined introns and destructive crossover in genetic programming, in Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, ed. by J.P. Rosca (Tahoe City, CA, 1995), pp. 6–22

  71. M. O’Neill, C. Ryan, Grammatical evolution. IEEE Trans. Evol. Comput. 5(4), 349–358 (2001)

    Article  Google Scholar 

  72. M. O’Neill, L. Vanneschi, S. Gustafson, W. Banzhaf, Open issues in genetic programming. Genet. Program. Evol. Mach. 11, 339–363 (2010)

    Article  Google Scholar 

  73. U.-M. O’Reilly, Using a distance metric on genetic programs to understand genetic operators, in IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, vol. 5, (Orlando, FL, 1997), pp. 4092–4097

  74. U.-M. O’Reilly, D.E. Goldberg, How fitness structure affects subsolution acquisition in genetic programming, in Genetic Programming 1998: Proceedings of the Third Annual Conference, ed. by J.R. Koza, et al. (Morgan Kaufmann, University of Wisconsin, Madison, 1998), pp. 269–277

  75. T.A. Pham, Q.U. Nguyen, X.H. Nguyen, M. O’Neill et al., Examining the diversity property of semantic similarity based crossover, in Genetic Programming, Volume 7831 of Lecture Notes in Computer Science, ed. by K. Krawiec (Springer, Berlin, 2013) pp. 265–276.

    Google Scholar 

  76. D.N. Phong, Q.U. Nguyen, X.H. Nguyen, R. McKay, Evolving approximations for the gaussian q-function by genetic programming with semantic based crossover, in Evolutionary Computation (CEC), 2012 IEEE Congress on (2012), pp. 1–6

  77. R. Poli, W.B. Langdon, N.F. Mcphee, A Field Guide to Genetic Programming (2008)

  78. W.F. Punch, D. Zongker, E.D. Goodman, The royal tree problem, a benchmark for single and multiple population genetic programming, in Advances in Genetic Programming 2, Chapter 15, ed. by P.J. Angeline, K.E. Kinnear Jr. (MIT Press, Cambridge, MA, 1996), pp. 299–316

  79. J.P. Rosca, Entropy-driven adaptive representation, in Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, ed. by J.P. Rosca (Tahoe City, CA, 1995), pp. 23–32

  80. J.P. Rosca, Genetic programming exploratory power and the discovery of functions, in Evolutionary Programming IV Proceedings of the Fourth Annual Conference on Evolutionary Programming, ed. by J.R. McDonnell, et al. (MIT Press, San Diego, CA, 1995), pp. 719–736

  81. C. Ryan, Pygmies and civil servants, in Advances in Genetic Programming, Chapter 11, ed. by K.E. Kinnear, Jr. (MIT Press, New York, 1994) pp. 243–263.

    Google Scholar 

  82. S. Silva, S. Dignum, Extending operator equalisation: fitness based self adaptive length distribution for bloat free GP, in Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009, Volume 5481 of LNCS, ed. by L. Vanneschi, et al. (Springer, Tuebingen, 2009), pp. 159–170

  83. S. Silva, S. Dignum, L. Vanneschi, Operator equalisation for bloat free genetic programming and a survey of bloat control methods. Genet. Program. Evol. Mach. 13(2), 197–238 (2012)

    Article  Google Scholar 

  84. S. Silva, V. Ingalalli, S. Vinga, J.A.M.B. Carreiras, J.B. Melo, M. Castelli, L. Vanneschi, I. Gonçalves, J. Caldas, Prediction of forest aboveground biomass: an exercise on avoiding overfitting, in Proceedings of the 16th European conference on Applications of Evolutionary Computation, EvoApplications’13 (Springer, Berlin, 2013), pp. 407–417

  85. A. Teller, M. Veloso, PADO: a new learning architecture for object recognition, in Symbolic Visual Learning, ed. by K. Ikeuchi, M. Veloso (Oxford University Press, Oxford, 1996) pp. 81–116.

    Google Scholar 

  86. M. Tomassini, L. Vanneschi, P. Collard, M. Clergue, A study of fitness distance correlation as a difficulty measure in genetic programming. Evol. Comput. 13(2), 213–239 (2005)

    Article  Google Scholar 

  87. L. Vanneschi, Theory and Practice for Efficient Genetic Programming. PhD thesis. (Faculty of Sciences, University of Lausanne, Switzerland, 2004)

  88. L. Vanneschi, M. Castelli, L. Manzoni, S. Silva, A new implementation of geometric semantic GP and its application to problems in pharmacokinetics, in Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013, Volume 7831 of LNCS, ed. by K. Krawiec, et al. (Springer, Vienna, 2013), pp. 205–216

  89. L. Vanneschi, S. Gustafson, G. Mauri, Using subtree crossover distance to investigate genetic programming dynamics, in Proceedings of the 9th European Conference on Genetic Programming, Volume 3905 of Lecture Notes in Computer Science, ed. by P. Collet, et al. (Springer, Budapest, 2006), pp. 238–249

  90. L. Vanneschi, S. Silva, M. Castelli, L. Manzoni, Geometric semantic genetic programming for real life applications, in Genetic Programming Theory and Practice XI, Genetic and Evolutionary Computation, ed. by R. Riolo, et al. (Springer US, Computer Science Collection, Ann Arbor, 2013). Invited article. To appear

  91. J. Wolberg, Data Analysis Using the Method of Least Squares: Extracting the Most Information from Experiments. (Springer, Heidelberg, 2006)

    Google Scholar 

  92. B. Wyns, P. De Bruyne, L. Boullart, Characterizing diversity in genetic programming, in Proceedings of the 9th European Conference on Genetic Programming, Volume 3905 of Lecture Notes in Computer Science, ed. by P. Collet, et al. (Springer, Budapest, 2006), pp. 250–259

  93. M. Zhang, P. Wong, D. Qian, Online program simplification in genetic programming, in Simulated Evolution and Learning, Volume 4247 of Lecture Notes in Computer Science, ed. by T.-D. Wang, et al. (Springer, Berlin, 2006), pp. 592–600

  94. Z. Zhu, A. Nandi, M. Aslam, Adapted geometric semantic genetic programming for diabetes and breast cancer classification, in Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on (2013), pp. 1–5

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Acknowledgments

This work was supported by national funds through FCT under contract PEst-OE/EEI/LA0021/2013 and by projects MassGP (PTDC/EEI-CTP/2975/2012), EnviGP (PTDC/EIA-CCO/103363/2008) and InteleGen (PTDC/DTP-FTO/1747/2012), Portugal.

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  1. ISEGI, Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal

    Leonardo Vanneschi & Mauro Castelli

  2. INESC-ID, IST, Universidade Técnica de Lisboa, 1000-029, Lisbon, Portugal

    Leonardo Vanneschi & Sara Silva

  3. CISUC, Universidade de Coimbra, 3030-290, Coimbra, Portugal

    Sara Silva

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Vanneschi, L., Castelli, M. & Silva, S. A survey of semantic methods in genetic programming. Genet Program Evolvable Mach 15, 195–214 (2014). https://doi.org/10.1007/s10710-013-9210-0

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