Turtles, termites, and traffic jams: explorations in massively parallel microworlds
Turtles, termites, and traffic jams: explorations in massively parallel microworlds
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
The ant colony optimization meta-heuristic
New ideas in optimization
Future Generation Computer Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Using a style-based ant colony system for adaptive learning
Expert Systems with Applications: An International Journal
An attribute-based ant colony system for adaptive learning object recommendation
Expert Systems with Applications: An International Journal
Adapting the ELO rating system to competing subpopulations in a “man-hill”
Proceedings of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering
Evolutionary computation approaches to the Curriculum Sequencing problem
Natural Computing: an international journal
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This paper describes experiments aimed at adapting Ant Colony Optimization (ACO) techniques to an e-learning environment, thanks to the fact that the available on-line material can be organized in a graph by means of hyperlinks between educational topics. The structure of this graph is to be optimized in order to facilitate the learning process for students.ACO is based on an ant-hill metaphor. In this case, however, the agents that move on the graph are students who unconsciously leave pheromones in the environment depending on their success or failure. In the paper, the whole process is therefore referred to as a "man-hill."Compared to the [13, 14] papers that were providing guidelines for this problem, real-size tests have been performed, showing that man-hills behave differently from ant-hills. The notion of pheromone erosion (rather than evaporation) is introduced.