A new implementation of geometric semantic GP and its application to problems in pharmacokinetics

Leonardo Vanneschi, Mauro Castelli, Luca Manzoni, Sara Silva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

88 Citations (Scopus)


Moraglio et al. have recently introduced new genetic operators for genetic programming, called geometric semantic operators. These operators induce a unimodal fitness landscape for all the problems consisting in matching input data with known target outputs (like regression and classification). This feature facilitates genetic programming evolvability, which makes these operators extremely promising. Nevertheless, Moraglio et al. leave open problems, the most important one being the fact that these operators, by construction, always produce offspring that are larger than their parents, causing an exponential growth in the size of the individuals, which actually renders them useless in practice. In this paper we overcome this limitation by presenting a new efficient implementation of the geometric semantic operators. This allows us, for the first time, to use them on complex real-life applications, like the two problems in pharmacokinetics that we address here. Our results confirm the excellent evolvability of geometric semantic operators, demonstrated by the good results obtained on training data. Furthermore, we have also achieved a surprisingly good generalization ability, a fact that can be explained considering some properties of geometric semantic operators, which makes them even more appealing than before.

Original languageEnglish
Title of host publicationGenetic Programming - 16th European Conference, EuroGP 2013, Proceedings
Number of pages12
Publication statusPublished - 22 Mar 2013
Event16th European Conference on Genetic Programming, EuroGP 2013 - Vienna, Austria
Duration: 3 Apr 20135 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7831 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th European Conference on Genetic Programming, EuroGP 2013


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