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A special issue of Intelligence & Robotics

ISSN 2770-3541 (Online)

Submission deadline: 31 Dec 2022

Guest Editor(s)

  • Assoc. Prof. Danilo Pelusi
    Faculty of Communication Sciences, University of Teramo, Teramo, Italy.

    Website | E-mail

Special Issue Introduction

Machine Learning (ML) is a branch of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The primary aim of ML is to allow computers to learn automatically without human intervention or assistance and adjust actions accordingly. This is accomplished by suitable training algorithms. The challenge is to design an optimal training algorithm able to learn from data. The optimization can happen with the help of Evolutionary Algorithms (EA). EA is a generic population-based metaheuristic optimization algorithm that uses mechanisms inspired by biological evolution. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. These evolutionary techniques maybe applied to machine learning for improving machine learning tasks such as classification, regression, and clustering. This special issue provides a platform for researchers from academia and industry to present their novel and unpublished works on evolutionary machine learning for engineering applications.

Potential topics include but are not limited to the following:
● Machine learning
● Neuro-fuzzy systems
● Evolutionary algorithms
● Optimization algorithms
● Deep learning
● Memetic algorithms
● Nature-inspired algorithms


Machine learning, neuro-fuzzy systems, soft computing, computational intelligence, evolutionary computation

Submission Deadline

31 Dec 2022

Submission Information

Articles of special issue are free of charge for article processing.
For Author Instructions, please refer to
For Online Submission, please login at
Submission Deadline: 31 Dec 2022
Contacts: Jane Pang, Assistant Editor,

Published Articles

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