In this chapter we focus on the importance of the use of learning and anticipation in (online) dynamic optimization. To this end we point out an important source of problem-difficulty 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 (i.e. anticipation) by learning from the past. We formalize this means in an algorithmic framework and indicate why evolutionary algorithms (EAs) are specifically of interest in this framework. We have performed experiments with two benchmark problems that feature time-linkage. The results show, as a proof of principle, that in the presence of time-linkage EAs based on this framework can obtain better results than classic EAs that do not predict the future.
Evolutionary Algorithms, Dynamic Optimization, Online Optimization, Learning, Predicting
ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY (acm F.2), SIMULATION AND MODELING (acm I.6)
Software (theme 1), Logistics (theme 3), Energy (theme 4)
Springer
S. Yang , Y.S. Ong , Y. Jin
Studies in computational intelligence
Decision Support Systems for Logistic Networks and Supply Chain Optimization
Intelligent and autonomous systems

Bosman, P.A.N. (2007). Learning and Anticipation in Online Dynamic Optimization. In S Yang, Y.S Ong, & Y Jin (Eds.), Evolutionary Computation in Dynamic and Uncertain Environments (pp. 129–152). Springer.