The influence of population size in geometric semantic GP

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


In this work, we study the influence of the population size on the learning ability of Geometric Semantic Genetic Programming for the task of symbolic regression. A large set of experiments, considering different population size values on different regression problems, has been performed. Results show that, on real-life problems, having small populations results in a better training fitness with respect to the use of large populations after the same number of fitness evaluations. However, performance on the test instances varies among the different problems: in datasets with a high number of features, models obtained with large populations present a better performance on unseen data, while in datasets characterized by a relative small number of variables a better generalization ability is achieved by using small population size values. When synthetic problems are taken into account, large population size values represent the best option for achieving good quality solutions on both training and test instances.

Original languageEnglish
Pages (from-to)110-120
Number of pages11
JournalSwarm and Evolutionary Computation
Publication statusPublished - 1 Feb 2017


  • Genetic programming
  • Population size
  • Semantics


Dive into the research topics of 'The influence of population size in geometric semantic GP'. Together they form a unique fingerprint.

Cite this