Leonardo Vanneschi, Associate Professor,
Doctor in Informatics (University of Lausanne - Switzerland)
My main research interests are: Machine Learning, study of Complex Systems, Data Mining, and in particular Evolutionary Computation. My work can be broadly partitioned into theoretical studies on the foundations of Evolutionary Computation, and applicative work. The former cover the study of the principles of functionings of Evoilutionary Algorithms, with the final objective of developing strategies able to outperform the traditional algorithms on significant sets of complex real-life applications. The latter covers covering several different fields among which computational biology, image processing, personalized medicine, engineering, economy and, recently, maritime safety and security. My work has been consistently recognized and appreciated by the international community from 2000 to nowadays.
Five Recent Publications
- 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.