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GECCO
2005
Springer

Learning, anticipation and time-deception in evolutionary online dynamic optimization

9 years 9 months ago
Learning, anticipation and time-deception in evolutionary online dynamic optimization
In this paper we focus on an important source of problem– difficulty in (online) dynamic optimization problems that has so far received significantly less attention than the traditional shifting of optima. Intuitively put, decisions taken now (i.e. setting the problem variables to certain values) may influence the score that can be obtained in the future. We indicate how such time–linkage can deceive an optimizer and cause it to find a suboptimal solution trajectory. We then propose a means to address time–linkage: predict the future by learning from the past. We formalize this means in an algorithmic framework. Also, we indicate why evolutionary algorithms are specifically of interest in this framework. We have performed experiments with two new benchmark problems that contain time–linkage. The results show, as a proof of principle, that in the presence of time–linkage EAs based upon this framework can obtain better results than classic EAs that do not predict the futur...
Peter A. N. Bosman
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where GECCO
Authors Peter A. N. Bosman
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