Both approaches delivered similar error rates. The Cox model gives a clinically understandable output on covariate impact, whereas RSF becomes more of a "black box." RSF complements the Cox model by giving more insight and confidence toward relative importance of model covariates. RSF can be recommended as the approach of choice in automating survival analyses.

Abstract. This study describes first steps taken to bring evolutionary optimization technology from computer simulations to real world experimentation in physics laboratories. The approach taken considers a well understood Laser Pulse Compression problem accessible both to simulation and laboratory experimentation as a test function for variants of Evolution Strategies. The main focus lies on coping with the unavoidable noise present in laboratory experimentation. Results from simulations are compared to previous studies and to laboratory experiments.

Abstract-Neural networks and the Kriging method are compared for constructing fitness approximation models in evolutionary optimization algorithms. The two models are applied in an identical framework to the optimization of a number of well known test functions. In addition, two different ways of training the approximators are evaluated: In one setting the models are built off-line using data from previous optimization runs and in the other setting the models are built online from the data available from the current optimization.

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