A hybrid Neural Network-Genetic Programming Intelligent Control approach

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

3 Citations (Scopus)
25 Downloads (Pure)


The proposed work aims to introduce a novel approach to Intelligent Control (IC), based on the combined use of Genetic Programming (GP) and feedforward Neural Network (NN). Both techniques have been successfully used in the literature for regression and control applications, but, while a NN creates a black box model, GP allows for a greater interpretability of the created model, which is a key feature in control applications. The main idea behind the hybrid approach proposed in this paper is to combine the speed and flexibility of a NN with the interpretability of GP. Moreover, to improve the robustness of the GP control law against unforeseen environmental changes, a new selection and crossover mechanisms, called Inclusive Tournament and Inclusive Crossover, are also introduced. The proposed IC approach is tested on the guidance control of a space transportation system and results, showing the potentialities for real applications, are shown and discussed.
Original languageEnglish
Title of host publicationBioinspired Optimization Methods and Their Applications
EditorsBogdan Filipič, Edmondo Minisci, Massimiliano Vasile
Place of PublicationCham, Switzerland
Number of pages15
ISBN (Print)9783030637101
Publication statusPublished - 16 Nov 2020
EventBioinspired Optimization Methods and their Applications 2020 - Université Libre de Bruxelles (Virtual), Bruxelles, Belgium
Duration: 19 Nov 202020 Nov 2020


ConferenceBioinspired Optimization Methods and their Applications 2020
Abbreviated titleBIOMA 2020
Internet address


  • Genetic Programming
  • Intelligent Control
  • Neural Networks
  • optimal control
  • space transportation system


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