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Models of Representation in Computational Intelligence [Guest Editorial]

Nobile, Marco S.; Manzoni, Luca; Ashlock, Daniel A.; Qu, Rong


Marco S. Nobile

Luca Manzoni

Daniel A. Ashlock

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Professor of Computer Science


Computational Intelligence (CI) provides a set of powerful tools to effectively tackle complex computational tasks: global optimization methods (e.g., evolutionary computation, swarm intelligence), machine learning (e.g., neural networks), fuzzy reasoning, and so on. While CI research generally focuses on the improvement of algorithms (e.g., faster convergence, higher accuracy, reduced error), another promising research direction concerns the representations and models in CI. This can be in the form of search space transformation, that is, dilating, shrinking, stretching, collapsing, or remapping the fitness landscape, leading to the simplified formulations of optimization problems. The use of surrogate modeling can further reduce the complexity or the computational effort of a CI task, by providing the optimization algorithm with a simplified or approximated version of the fitness landscape. Moreover, in discrete domains, the simplification of the problem can be obtained by embedding implicit or explicit assumptions into the structure of candidate solutions, so that the feasible search space can be explored by genetic operators in a 'smarter' way, reducing the overall computational effort. In the contexts of machine learning or fuzzy modeling, the focus can be on interpretability and explainability, the two open issues that currently affect the trust in AI solutions and hamper the adoption of such techniques in some disciplines (in particular, biomedical applications).


Nobile, M. S., Manzoni, L., Ashlock, D. A., & Qu, R. (2023). Models of Representation in Computational Intelligence [Guest Editorial]. IEEE Computational Intelligence Magazine, 18(1), 20-21.

Journal Article Type Editorial
Acceptance Date Jan 25, 2023
Online Publication Date Jan 25, 2023
Publication Date Feb 1, 2023
Deposit Date Jan 29, 2023
Journal IEEE Computational Intelligence Magazine
Print ISSN 1556-603X
Electronic ISSN 1556-6048
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Not Peer Reviewed
Volume 18
Issue 1
Pages 20-21
Keywords Special issues and sections , Computational intelligence , Modeling , Computer applications
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