AAAI Publications, The Thirtieth International Flairs Conference

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Evolutionary Practice Problems Generation: More Design Guidelines
Alessio Gaspar, A.T. M. Golam Bari, R. Paul Wiegand, Anthony Bucci, Amruth N. Kumar, Jennifer L. Albert

Last modified: 2017-05-08


We propose to further extend preliminary investigations of the nature of the problem of evolving practice problems for learners. Using a refinement of a previous simple model of interaction between learners and practice problems, we examine some of its properties and experimentally highlight the role played by the number of values each gene may take in our encoding of practice problems. We then experimentally compare both a traditional - P-CHC - and Pareto-based - P-PHC - variants of coevolutionary algorithms. Comparisons are conducted with respect to the presence of noise in fitness evaluations, the number of values genes may take, and two distinct fitness functions. Each fitness captures an aspect of the nature of learner-problem interaction but one has been shown to induce overspecialization pathologies. We then summarize our findings in terms of guidelines on how to adapt evolutionary algorithms to tackle the task of evolving practice problems.


Coevolutionary Algorithms, Interactive Evolutionary Algorithms, User Fatigue, Computer-Aided Learning, Intelligent Tutoring System

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