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2 2013

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Incremental rule-based learners for handling concept drift: an overview

Deckert M

Foundations of Computing and Decision Sciences

2013

Vol. 38, No. 1

35--65

Learning from non-stationary environments is a very popular research topic. There already exist algorithms that deal with the concept drift problem. Among them there are online or incremental learners, which process data instance by instance. Their knowledge representation can take different forms such as decision rules, which have not received enough attention in learning with concept drift. This paper reviews incremental rule-based learners designed for changing environments. It describes four of the proposed algorithms: FLORA, AQ11-PM+WAH, FACIL and VFDR. Those four solutions can be compared on several criteria, like: type of processed data, adjustment to changes, type of the maintained memory, knowledge representation, and others.

Evolutionary multi-agent systems in non-stationary environments

Kisiel-Dorohinicki M.

Computer Science

2013

Vol. 14 (4)

563--575

In this article, the performance of an evolutionary multi-agent system in dynamic optimization is evaluated in comparison to classical evolutionary algorithms. The starting point is a general introduction describing the background, structure and behavior of EMAS against classical evolutionary techniques. Then, the properties of energy-based selection are investigated to show how they may influence the diversity of the population in EMAS. The considerations are illustrated by experimental results based on the dynamic version of the well-known, high-dimensional Rastrigin function benchmark.