Geometric semantic genetic programming for biomedical applications: A state of the art upgrade

Leonardo Vanneschi, Mauro Castelli, Ivo Goncalves, Luca Manzoni, Sara Silva

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Geometric semantic genetic programming is a hot topic in evolutionary computation and recently it has been used with success on several problems from Biology and Medicine. Given the young age of geometric semantic genetic programming, in the last few years theoretical research, aimed at improving the method, and applicative research proceeded rapidly and in parallel. As a result, the current state of the art is confused and presents some 'holes'. For instance, some recent improvements of geometric semantic genetic programming have never been applied to some popular biomedical applications. The objective of this paper is to fill this gap. We consider the biomedical applications that have more frequently been used by genetic programming researchers in the last few years and we systematically test, in a consistent way, using the same parameter settings and configurations, all the most popular existing variants of geometric semantic genetic programming on all those applications. Analysing all these results, we obtain a much more homogeneous and clearer picture of the state of the art, that allows us to draw stronger conclusions.

Original languageEnglish
Title of host publication2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages177-184
Number of pages8
ISBN (Electronic)9781509046010
DOIs
Publication statusPublished - 5 Jul 2017
Event2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Donostia-San Sebastian, Spain
Duration: 5 Jun 20178 Jun 2017

Conference

Conference2017 IEEE Congress on Evolutionary Computation, CEC 2017
CountrySpain
CityDonostia-San Sebastian
Period5/06/178/06/17

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