Integration of evolutionary algorithm in an agent-oriented approach for an adaptive e-learning

Fatima Zohra Lhafra, Otman Abdoun


This paper describes an agent-oriented approach that aims to create learning situations by solving problems. The proposed system is designed as a multi-agent that organizes interfaces, coordinators, sources of information, and mobiles. The objective of this approach is to get learners to solve a problem that leads them to get engaged in several learning activities, chosen according to their level of knowledge and preferences in order to ensure adaptive learning and reduce the rate of learner abundance in an e-learning system. The search for learning activities procedure is based on evolutionary algorithms typically a genetic algorithm, to offer learners the optimal solution adapted to their profiles and ensure a resolution of the proposed learning problem. In terms of results, we have adopted “immigration strategies” to improve the performance of the genetic algorithm. To show the effectiveness of the proposed approach we have made a comparative study with other artificial intelligence optimization methods. We conducted a real experiment with primary school learners in order to test the effectiveness of the proposed approach and to set up its functioning. The experiment results showed a high rate of success and engagement among the learners who followed the proposed adaptive learning scenario.


adaptive learning; ant colony optimization; e-learning; genetic algorithm; learning by solving problems evolutionary algorithm; multi-agent system; particle swarm optimization immigration operator;

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).