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Leonardo Vanneschi: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Stefania Bandini, Sara Manzoni, Stefano Redaelli, Leonardo Vanneschi
    Automatic Detection of Go-Based Patterns in CA Model of Vegetable Populations: Experiments on Geta Pattern Recognition. [Citation Graph (0, 0)][DBLP]
    ACRI, 2006, pp:427-435 [Conf]
  2. Sébastien Vérel, Philippe Collard, Marco Tomassini, Leonardo Vanneschi
    Neutral Fitness Landscape in the Cellular Automata Majority Problem. [Citation Graph (0, 0)][DBLP]
    ACRI, 2006, pp:258-267 [Conf]
  3. Marco Tomassini, Leonardo Vanneschi, Francisco Fernández, Germán Galeano Gil
    A Study of Diversity in Multipopulation Genetic Programming. [Citation Graph (0, 0)][DBLP]
    Artificial Evolution, 2003, pp:243-255 [Conf]
  4. Leonardo Vanneschi, Marco Tomassini, Philippe Collard, Manuel Clergue
    A Survey of Problem Difficulty in Genetic Programming. [Citation Graph (0, 0)][DBLP]
    AI*IA, 2005, pp:66-77 [Conf]
  5. Francisco Fernández de Vega, Marco Tomassini, Leonardo Vanneschi
    Studying the Influence of Communication Topology and Migration on Distributed Genetic Programming. [Citation Graph (0, 0)][DBLP]
    EuroGP, 2001, pp:51-63 [Conf]
  6. Steven M. Gustafson, Leonardo Vanneschi
    Operator-Based Distance for Genetic Programming: Subtree Crossover Distance. [Citation Graph (0, 0)][DBLP]
    EuroGP, 2005, pp:178-189 [Conf]
  7. Denis Rochat, Marco Tomassini, Leonardo Vanneschi
    Dynamic Size Populations in Distributed Genetic Programming. [Citation Graph (0, 0)][DBLP]
    EuroGP, 2005, pp:50-61 [Conf]
  8. Leonardo Vanneschi, Steven Gustafson, Giancarlo Mauri
    Using Subtree Crossover Distance to Investigate Genetic Programming Dynamics. [Citation Graph (0, 0)][DBLP]
    EuroGP, 2006, pp:238-249 [Conf]
  9. Leonardo Vanneschi, Marco Tomassini, Philippe Collard, Manuel Clergue
    Fitness Distance Correlation in Structural Mutation Genetic Programming. [Citation Graph (0, 0)][DBLP]
    EuroGP, 2003, pp:455-464 [Conf]
  10. Leonardo Vanneschi, Marco Tomassini, Philippe Collard, Sébastien Vérel
    Negative Slope Coefficient: A Measure to Characterize Genetic Programming Fitness Landscapes. [Citation Graph (0, 0)][DBLP]
    EuroGP, 2006, pp:178-189 [Conf]
  11. Francisco Fernández de Vega, Leonardo Vanneschi, Marco Tomassini
    The Effect of Plagues in Genetic Programming: A Study of Variable-Size Populations. [Citation Graph (0, 0)][DBLP]
    EuroGP, 2003, pp:317-326 [Conf]
  12. Francesco Archetti, Enza Messina, Daniele Toscani, Leonardo Vanneschi
    Classifying and Counting Vehicles in Traffic Control Applications. [Citation Graph (0, 0)][DBLP]
    EvoWorkshops, 2006, pp:495-499 [Conf]
  13. Francesco Archetti, Stefano Lanzeni, Enza Messina, Leonardo Vanneschi
    Genetic programming for human oral bioavailability of drugs. [Citation Graph (0, 0)][DBLP]
    GECCO, 2006, pp:255-262 [Conf]
  14. Manuel Clergue, Philippe Collard, Marco Tomassini, Leonardo Vanneschi
    Fitness Distance Correlation And Problem Difficulty For Genetic Programming. [Citation Graph (0, 0)][DBLP]
    GECCO, 2002, pp:724-732 [Conf]
  15. Mario Giacobini, Marco Tomassini, Leonardo Vanneschi
    How Statistics Can Help In Limiting The Number Of Fitness Cases In Genetic Programming. [Citation Graph (0, 0)][DBLP]
    GECCO, 2002, pp:889- [Conf]
  16. Marco Tomassini, Leonardo Vanneschi, Francisco Fernández de Vega, Germán Galeano Gil
    Diversity in Multipopulation Genetic Programming. [Citation Graph (0, 0)][DBLP]
    GECCO, 2003, pp:1812-1813 [Conf]
  17. Leonardo Vanneschi, Manuel Clergue, Philippe Collard, Marco Tomassini, Sébastien Vérel
    Fitness Clouds and Problem Hardness in Genetic Programming. [Citation Graph (0, 0)][DBLP]
    GECCO (2), 2004, pp:690-701 [Conf]
  18. Leonardo Vanneschi, Giancarlo Mauri, Andrea Valsecchi, Stefano Cagnoni
    Heterogeneous cooperative coevolution: strategies of integration between GP and GA. [Citation Graph (0, 0)][DBLP]
    GECCO, 2006, pp:361-368 [Conf]
  19. Leonardo Vanneschi, Yuri Pirola, Philippe Collard
    A quantitative study of neutrality in GP boolean landscapes. [Citation Graph (0, 0)][DBLP]
    GECCO, 2006, pp:895-902 [Conf]
  20. Leonardo Vanneschi, Marco Tomassini, Manuel Clergue, Philippe Collard
    Difficulty of Unimodal and Multimodal Landscapes in Genetic Programming. [Citation Graph (0, 0)][DBLP]
    GECCO, 2003, pp:1788-1799 [Conf]
  21. Stefania Bandini, Sara Manzoni, Stefano Redaelli, Leonardo Vanneschi
    Emergent Spatial Patterns in Vegetable Population Dynamics: Towards Pattern Detection and Interpretation. [Citation Graph (0, 0)][DBLP]
    International Conference on Computational Science (3), 2006, pp:289-296 [Conf]
  22. Mario Giacobini, Marco Tomassini, Leonardo Vanneschi
    Limiting the Number of Fitness Cases in Genetic Programming Using Statistics. [Citation Graph (0, 0)][DBLP]
    PPSN, 2002, pp:371-380 [Conf]
  23. Marco Tomassini, Leonardo Vanneschi, Francisco Fernández de Vega, Germán Galeano Gil
    Experimental Investigation of Three Distributed Genetic Programming Models. [Citation Graph (0, 0)][DBLP]
    PPSN, 2002, pp:641-650 [Conf]
  24. Francisco Fernández de Vega, Marco Tomassini, Leonardo Vanneschi, Laurent Bucher
    A Distributed Computing Environment for Genetic Programming Using MPI. [Citation Graph (0, 0)][DBLP]
    PVM/MPI, 2000, pp:322-329 [Conf]
  25. Marco Tomassini, Leonardo Vanneschi, Philippe Collard, Manuel Clergue
    A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming. [Citation Graph (0, 0)][DBLP]
    Evolutionary Computation, 2005, v:13, n:2, pp:213-239 [Journal]
  26. Francisco Fernández de Vega, Marco Tomassini, Leonardo Vanneschi
    An Empirical Study of Multipopulation Genetic Programming. [Citation Graph (0, 0)][DBLP]
    Genetic Programming and Evolvable Machines, 2003, v:4, n:1, pp:21-51 [Journal]
  27. Leonardo Vanneschi, Marco Tomassini, Philippe Collard, Sébastien Vérel, Yuri Pirola, Giancarlo Mauri
    A Comprehensive View of Fitness Landscapes with Neutrality and Fitness Clouds. [Citation Graph (0, 0)][DBLP]
    EuroGP, 2007, pp:241-250 [Conf]
  28. Francesco Archetti, Stefano Lanzeni, Enza Messina, Leonardo Vanneschi
    Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs. [Citation Graph (0, 0)][DBLP]
    EvoBIO, 2007, pp:11-23 [Conf]
  29. Riccardo Poli, Leonardo Vanneschi
    Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms. [Citation Graph (0, 0)][DBLP]
    GECCO, 2007, pp:1335-1342 [Conf]
  30. Leonardo Vanneschi, Sébastien Vérel
    Fitness landscapes and problem hardness in evolutionary computation. [Citation Graph (0, 0)][DBLP]
    GECCO (Companion), 2007, pp:3690-3733 [Conf]
  31. Leonardo Vanneschi, Denis Rochat, Marco Tomassini
    Multi-optimization improves genetic programming generalization ability. [Citation Graph (0, 0)][DBLP]
    GECCO, 2007, pp:1759- [Conf]
  32. Stefano Cagnoni, Daniel Rivero, Leonardo Vanneschi
    A purely evolutionary memetic algorithm as a first step towards symbiotic coevolution. [Citation Graph (0, 0)][DBLP]
    Congress on Evolutionary Computation, 2005, pp:1156-1163 [Conf]
  33. Sébastien Vérel, Philippe Collard, Marco Tomassini, Leonardo Vanneschi
    Fitness landscape of the cellular automata majority problem: View from the Olympus [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  34. Sébastien Vérel, Philippe Collard, Marco Tomassini, Leonardo Vanneschi
    Fitness landscape of the cellular automata majority problem: View from the "Olympus". [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 2007, v:378, n:1, pp:54-77 [Journal]

  35. GP Generation of Pedestrian Behavioral Rules in an Evacuation Model Based on SCA. [Citation Graph (, )][DBLP]


  36. A Study on the Automatic Generation of Asynchronous Cellular Automata Rules by Means of Genetic Algorithms. [Citation Graph (, )][DBLP]


  37. An MPI-Based Tool for Distributed Genetic Programming. [Citation Graph (, )][DBLP]


  38. Using Operator Equalisation for Prediction of Drug Toxicity with Genetic Programming. [Citation Graph (, )][DBLP]


  39. Negative Slope Coefficient and the Difficulty of Random 3-SAT Instances. [Citation Graph (, )][DBLP]


  40. A Critical Assessment of Some Variants of Particle Swarm Optimization. [Citation Graph (, )][DBLP]


  41. A Study of Some Implications of the No Free Lunch Theorem. [Citation Graph (, )][DBLP]


  42. An Evolutionary Framework for Colorimetric Characterization of Scanners. [Citation Graph (, )][DBLP]


  43. Genetic Algorithms for Training Data and Polynomial Optimization in Colorimetric Characterization of Scanners. [Citation Graph (, )][DBLP]


  44. NK Landscapes Difficulty and Negative Slope Coefficient: How Sampling Influences the Results. [Citation Graph (, )][DBLP]


  45. A Comparison of Genetic Algorithms and Particle Swarm Optimization for Parameter Estimation in Stochastic Biochemical Systems. [Citation Graph (, )][DBLP]


  46. Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques. [Citation Graph (, )][DBLP]


  47. Elitism reduces bloat in genetic programming. [Citation Graph (, )][DBLP]


  48. The impact of population size on code growth in GP: analysis and empirical validation. [Citation Graph (, )][DBLP]


  49. Using crossover based similarity measure to improve genetic programming generalization ability. [Citation Graph (, )][DBLP]


  50. Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction. [Citation Graph (, )][DBLP]


  51. Limitations of the fitness-proportional negative slope coefficient as a difficulty measure. [Citation Graph (, )][DBLP]


  52. Variable size population for dynamic optimization with genetic programming. [Citation Graph (, )][DBLP]


  53. Fitness landscapes and problem hardness in genetic programming. [Citation Graph (, )][DBLP]


  54. Measuring bloat, overfitting and functional complexity in genetic programming. [Citation Graph (, )][DBLP]


  55. An empirical comparison of parallel and distributed particle swarm optimization methods. [Citation Graph (, )][DBLP]


  56. Definition of a crossover based distance for genetic algorithms. [Citation Graph (, )][DBLP]


  57. On the use of genetic programming for the prediction of survival in cancer. [Citation Graph (, )][DBLP]


  58. Optimization speed and fair sets of functions. [Citation Graph (, )][DBLP]


  59. Fitness landscapes and problem hardness in genetic programming. [Citation Graph (, )][DBLP]


  60. Empirical modeling for colorimetric characterization of digital cameras. [Citation Graph (, )][DBLP]


  61. Diversity analysis in cellular and multipopulation genetic programming. [Citation Graph (, )][DBLP]


  62. Fitness distance correlation in genetic programming: a constructive counterexample. [Citation Graph (, )][DBLP]


  63. A Study of Genetic Programming Variable Population Size for Dynamic Optimization Problems. [Citation Graph (, )][DBLP]


  64. Genetic programming for QSAR investigation of docking energy. [Citation Graph (, )][DBLP]


  65. Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset. [Citation Graph (, )][DBLP]


  66. Neutral Fitness Landscape in the Cellular Automata Majority Problem [Citation Graph (, )][DBLP]


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