Evolutionary Practice Problems Generation: More Design Guidelines

Abstract

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 cevolutionary 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.

Publication Date

1-1-2017

Publication Title

FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference

Number of Pages

549-554

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

85029517486 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85029517486

This document is currently not available here.

Share

COinS