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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

An Introduction to Geometric Semantic Genetic Programming
Leonardo Vanneschi
Studies in computational intelligence (2016), pp. 3-42
Closed Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

An artificial intelligence system for predicting customer default in e-commerce
Leonardo Vanneschi, David M. Horn, Mauro Castelli, et al.
Expert Systems with Applications (2018) Vol. 104, pp. 1-21
Closed Access | Times Cited: 82
Burned area estimations derived from Landsat ETM+ and OLI data: Comparing Genetic Programming with Maximum Likelihood and Classification and Regression Trees
Ana I. R. Cabral, Sara Silva, Pedro C. Silva, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2018) Vol. 142, pp. 94-105
Open Access | Times Cited: 67
Exploring Non-bloating Geometric Semantic Genetic Programming
Leonardo Vanneschi, Davide Farinati, Deolinda Rasteiro, et al.
Genetic and evolutionary computation (2025), pp. 237-258
Closed Access
Sharpness-Aware Minimization in Genetic Programming
Illya Bakurov, Nathan Haut, Wolfgang Banzhaf
Genetic and evolutionary computation (2025), pp. 151-175
Closed Access
Improving Generalization of Genetic Programming for Symbolic Regression With Angle-Driven Geometric Semantic Operators
Qi Chen, Bing Xue, Mengjie Zhang
IEEE Transactions on Evolutionary Computation (2018) Vol. 23, Iss. 3, pp. 488-502
Closed Access | Times Cited: 55
SLIM_GSGP: The Non-bloating Geometric Semantic Genetic Programming
Leonardo Vanneschi
Lecture notes in computer science (2024), pp. 125-141
Closed Access | Times Cited: 2
An expert system for extracting knowledge from customers’ reviews: The case of Amazon.com, Inc.
Mauro Castelli, Luca Manzoni, Leonardo Vanneschi, et al.
Expert Systems with Applications (2017) Vol. 84, pp. 117-126
Open Access | Times Cited: 26
A study of dynamic populations in geometric semantic genetic programming
Davide Farinati, Illya Bakurov, Leonardo Vanneschi
Information Sciences (2023) Vol. 648, pp. 119513-119513
Open Access | Times Cited: 7
Forecasting performance of regional innovation systems using semantic-based genetic programming with local search optimizer
Petr Hájek, Roberto Henriques, Mauro Castelli, et al.
Computers & Operations Research (2018) Vol. 106, pp. 179-190
Open Access | Times Cited: 21
GSGP-CUDA — A CUDA framework for Geometric Semantic Genetic Programming
Leonardo Trujillo, José Manuel Muñoz Contreras, Daniel E. Hernández, et al.
SoftwareX (2022) Vol. 18, pp. 101085-101085
Open Access | Times Cited: 11
Ensemble Genetic Programming
Nuno M. Rodrigues, João E. Batista, Sara Silva
Lecture notes in computer science (2020), pp. 151-166
Closed Access | Times Cited: 17
An initialization technique for geometric semantic GP based on demes evolution and despeciation
Leonardo Vanneschi, Illya Bakurov, Mauro Castelli
2022 IEEE Congress on Evolutionary Computation (CEC) (2017), pp. 113-120
Closed Access | Times Cited: 18
Solving the exponential growth of symbolic regression trees in geometric semantic genetic programming
Joao Francisco B. S. Martins, Luiz Otávio V. B. Oliveira, Luis F. Miranda, et al.
Proceedings of the Genetic and Evolutionary Computation Conference (2018), pp. 1151-1158
Open Access | Times Cited: 17
Benchmarking GSGP: Still competitive 10 years later?
José Manuel Muñoz Contreras, Leonardo Trujillo, Daniel E. Hernández, et al.
Genetic Programming and Evolvable Machines (2024) Vol. 26, Iss. 1
Closed Access | Times Cited: 1
General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python
Illya Bakurov, Marco Buzzelli, Mauro Castelli, et al.
Applied Sciences (2021) Vol. 11, Iss. 11, pp. 4774-4774
Open Access | Times Cited: 10
Genetic programming with semantic equivalence classes
Stefano Ruberto, Leonardo Vanneschi, Mauro Castelli
Swarm and Evolutionary Computation (2018) Vol. 44, pp. 453-469
Open Access | Times Cited: 10
A hierarchical estimation of multi-modal distribution programming for regression problems
Mohaddeseh Koosha, Ghazaleh Khodabandelou, Mohammad Mehdi Ebadzadeh
Knowledge-Based Systems (2022) Vol. 260, pp. 110129-110129
Closed Access | Times Cited: 6
Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System
Leonardo Vanneschi, Mauro Castelli, Kristen M. Scott, et al.
International Journal of Concrete Structures and Materials (2018) Vol. 12, Iss. 1
Open Access | Times Cited: 9
EDDA-V2 – An Improvement of the Evolutionary Demes Despeciation Algorithm
Illya Bakurov, Leonardo Vanneschi, Mauro Castelli, et al.
Lecture notes in computer science (2018), pp. 185-196
Closed Access | Times Cited: 8
Evolving PSO algorithm design in vector fields using geometric semantic GP
Palina Bartashevich, Illya Bakurov, Sanaz Mostaghim, et al.
Proceedings of the Genetic and Evolutionary Computation Conference Companion (2018), pp. 262-263
Closed Access | Times Cited: 6
An Investigation of Geometric Semantic GP with Linear Scaling
Giorgia Nadizar, Fraser Garrow, Berfin Sakallioglu, et al.
Proceedings of the Genetic and Evolutionary Computation Conference (2023), pp. 1165-1174
Open Access | Times Cited: 2
Geometric semantic genetic programming for biomedical applications: A state of the art upgrade
Leonardo Vanneschi, Mauro Castelli, Ivo Gonçalves, et al.
2022 IEEE Congress on Evolutionary Computation (CEC) (2017)
Closed Access | Times Cited: 4
Location, Size, and Capacity
Abdul Hanif Abdul Halim, Swagatam Das, Idris Ismail
Emergence, complexity and computation (2024), pp. 1-152
Closed Access
A parallel and distributed semantic Genetic Programming system
Leonardo Vanneschi, Bernardo Gil Câmara Galvão
2022 IEEE Congress on Evolutionary Computation (CEC) (2017), pp. 121-128
Closed Access | Times Cited: 3
PSO-Based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic Programming
Palina Bartashevich, Illya Bakurov, Sanaz Mostaghim, et al.
Lecture notes in computer science (2018), pp. 41-53
Closed Access | Times Cited: 3
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