Multi-agent memetic computing for adaptive learning experiences

G. Acampora, V. Loia, M. Gaeta, A. Vitiello

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)


Learning is a mechanism to acquire new knowledge and to enhance individual skills in industrial and academic environments. In particular, employing learning methods in an industrial context supports the overall business competitiveness in the new economy. Currently, the e-Learning systems provide a simple "digitalization" of the learning process where the focus is on the educational resources, which are only an input of the whole learning process, and on their presentation (delivery). Computational Intelligence methodologies can overcome current learning systems limitations attaining to personalize learning content and activities to specific preferences of the learner and to assist designers with computationally efficient methods to develop "in time" e-Learning environments. This paper shows how to achieve both results exploiting an ontological representation of learning environment and memetic approach of optimization, integrated into a cooperative distributed problem solving framework.
Original languageEnglish
Title of host publicationProceedings of the 2010 IEEE International Conference on Fuzzy Systems (FUZZ), 18-23 July 2010, Barcelona, Spain
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4244-6919-2
Publication statusPublished - 2010


Dive into the research topics of 'Multi-agent memetic computing for adaptive learning experiences'. Together they form a unique fingerprint.

Cite this