Leonardo Vanneschi

  • Friday, 18 July 2014 14:38

leonardo minIdentification

Leonardo Vanneschi, Associate Professor,
Doctor in Informatics (University of Lausanne - Switzerland)


Telephone: 213828610209
Fax: 213828611
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Research Interests

Leonardo Vanneschi is an Associate Professor with Tenure ("Professor Associado com Agregação") at the NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Portugal. His main research interests involve Machine Learning, Complex Systems, Data Mining, and in particular Evolutionary Computation. His work can be broadly partitioned into theoretical studies on the foundations of Evolutionary Computation, and applicative work. The former covers the study of the principles of functioning of Evolutionary Algorithms, with the final objective of developing strategies able to outperform the traditional techniques. The latter covers several different fields among which computational biology, image processing, personalized medicine, engineering, economics and maritime safety and security. His work has been consistently recognized and appreciated by the international community from 2000 to nowadays. In 2015, he was honoured with the Award for Outstanding Contributions to Evolutionary Computation in Europe, in the context of EvoStar, the leading European Event on Bio-Inspired Computation.

  • 1) M. Castelli, L. Vanneschi, and S. Silva. Prediction of the Unified Parkinson's Disease Rating Scale Assessment using a Genetic Programming System with Geometric Semantic Genetic Operators. Expert Systems with Applications, vol. 41, no. 10, pp. 4608 – 4616, 2014.
  • 2) M. Castelli, L. Vanneschi, and S. Silva. Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators. Expert Systems with Applications, vol. 40, no. 17, pp. 6856 – 6862, 2013.
  • 3) L. Vanneschi, M. Mondini, M. Bertoni, A. Ronchi, and M. Stefano. Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches. Genetic Programming and Evolvable Machines, vol. 14, no. 4, pp. 431–455, 2013.
  • 4) S. Silva and L. Vanneschi. Bloat free genetic programming: application to human oral bioavailability prediction. Int. J. Data Min. Bioinformatics, 6(6):585–601, Nov. 2012.
  • 5) L. Vanneschi, Y. Pirola, G. Mauri, M. Tomassini, P. Collard, and S. Verel. A study of the neutrality of boolean function landscapes in genetic programming. Theoretical Computer Science, 425:34–57, Mar. 2012.
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