Genetic programming in the 21st century: a bibliometric and content-based analysis from both sides of the fence




Andrea De Lorenzo, Alberto Bartoli, Eric Medvet, Mauro Castelli, Bing Xue


Genetic Programming and Evolvable Machines (GENP)
(rank Q2 in Computer Science Applications)



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Abstract #

In this work we present an extensive bibliometric and content-based analysis of the scientific literature about genetic programming in the 21st century. Our work has two key peculiarities. First, we revealed the topics emerging from the literature based on an unsupervised analysis of the textual content of titles and abstracts. Second, we executed all of our analyses twice. On the papers published on the venues that are typical of the evolutionary computation research community and those published on all the other venues. This view from “both sides of the fence” allows us to gain broader and deeper insights into the actual contributions of our community.