Abstract
The task of speeding up the optimization process on problems with very time consuming fitness functions is a central point in evolutionary computation. Applying models as a surrogate of the real fitness function is a quite popular idea. The performance of this approach is highly dependent on the frequency of how often the model is updated with data from new fitness evaluations. However, in generation based algorithms this is only done every λ-th fitness evaluation. To overcome this problem we use a steady-state strategy, which updates the model immediately after each fitness evaluation. We present a new model assisted steady-state Evolution Strategy (ES), which uses Radial-Basis-Function networks as a model. To support self-adaption in the steady-state algorithm a median selection scheme is applied. The convergence behavior of the new algorithm is examined with numerical results from extensive simulations on several high dimensional test functions. It achieves better results than standard ES, steady -state ES or model assisted ES.
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Ulmer, H., Streichert, F., Zell, A. (2003). Model-Assisted Steady-State Evolution Strategies. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_72
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DOI: https://doi.org/10.1007/3-540-45105-6_72
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