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Model-Assisted Steady-State Evolution Strategies
Holger Ulmer, Felix Streichert, and Andreas Zell
Center for Bioinformatics Tübingen (ZBIT),
University of Tübingen,
Sand 1,
72074 Tübingen, Germany,
{ulmerh,streiche,zell}@informatik.uni-tuebingen.de,
http://www-ra.informatik.uni-tuebingen.de
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 
-th fitness evaluation. To overcome this
problem we use a steady-state strategy, which updates the model immediately
after each fitness evaluation. We present anew 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.
LNCS 2723, p. 610 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003