Finding robust solutions to dynamic optimization problems

Authors:
Haobo Fu;Bernhard Sendhoff;Ke Tang;Xin Yao
Affiliations:
CERCIA, School of Computer Science, University of Birmingham, UK;Honda Research Institute Europe, Offenbach, Germany;Joint USTC-Birmingham Research Institute in Intelligent Computation and Its Applications, School of Computer Science and Technology, University of Science and Technology of China, China;CERCIA, School of Computer Science, University of Birmingham, UK
Venue:
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Year:
2013

Citing 6
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Abstract

Most research in evolutionary dynamic optimization is based on the assumption that the primary goal in solving Dynamic Optimization Problems (DOPs) is Tracking Moving Optimum (TMO). Yet, TMO is impractical in cases where keeping changing solutions in use is impossible. To solve DOPs more practically, a new formulation of DOPs was proposed recently, which is referred to as Robust Optimization Over Time (ROOT). In ROOT, the aim is to find solutions whose fitnesses are robust to future environmental changes. In this paper, we point out the inappropriateness of existing robustness definitions used in ROOT, and therefore propose two improved versions, namely survival time and average fitness. Two corresponding metrics are also developed, based on which survival time and average fitness are optimized respectively using population-based algorithms. Experimental results on benchmark problems demonstrate the advantages of our metrics over existing ones on robustness definitions survival time and average fitness.