Efficient relevance estimation and value calibration of evolutionary algorithm parameters

Volker Nannen, A. E. Eiben
2007 2007 IEEE Congress on Evolutionary Computation  
Calibrating the parameters of an evolutionary algorithm (EA) is a laborious task. The highly stochastic nature of an EA typically leads to a high variance of the measurements. The standard statistical method to reduce variance is measurement replication, i.e., averaging over several test runs with identical parameter settings. The computational cost of measurement replication scales with the variance and is often too high to allow for results of statistical significance. In this paper we study
more » ... n alternative: the REVAC method for Relevance Estimation and Value Calibration, and we investigate how different levels of measurement replication influence the cost and quality of its calibration results. Two sets of experiments are reported: calibrating a genetic algorithm on standard benchmark problems, and calibrating a complex simulation in evolutionary agent-based economics. We find that measurement replication is not essential to REVAC, which emerges as a strong and efficient alternative to existing statistical methods.
doi:10.1109/cec.2007.4424460 dblp:conf/cec/NannenE07 fatcat:vwgvzwgrmnbinln6vy4ufgaoni