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.
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Fu, H., Sendhoff, B., Tang, K., Yao, X. (2013). Finding Robust Solutions to Dynamic Optimization Problems. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_62
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DOI: https://doi.org/10.1007/978-3-642-37192-9_62
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