Towards the use of genetic programming in the ecological modelling of mosquito population dynamics

Irene Azzali, Leonardo Vanneschi, Andrea Mosca, Luigi Bertolotti, Mario Giacobini

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Predictive algorithms are powerful tools to support infection surveillance plans based on the monitoring of vector abundance. In this article, we explore the use of genetic programming (GP) to build a predictive model of mosquito abundance based on environmental and climatic variables. We claim, in fact, that the heterogeneity and complexity of this kind of dataset demands algorithms capable of discovering complex relationships among variables. For this reason, we benchmarked GP performance with state of the art machine learning predictive algorithms. In order to provide a real exploitable model of mosquito abundance, we trained GP and the other algorithms on mosquito collections from 2002 to 2005 and we tested the predictive ability in 2006 collections. Results reveal that, among the studied methods, GP has the best performance in terms of accuracy and generalization ability. Moreover, the intrinsic feature selection and readability of the solution provided by GP offer the possibility of a biological interpretation of the model which highlights known or new behaviours responsible for mosquito abundance. GP, therefore, reveals to be a promising tool in the field of ecological modelling, opening the way to the use of a vector based GP approach (VE-GP) which may be more appropriate and beneficial for the problems in analysis.

Original languageEnglish
Pages (from-to)629–642
Number of pages14
JournalGenetic Programming And Evolvable Machines
Issue number4
Early online date3 Jan 2020
Publication statusPublished - Dec 2020


  • Ecological modelling
  • Genetic programming
  • Machine learning
  • Regression


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