SDR: a better trigger for adaptive variance scaling in normal EDAs

Recently, advances have been made in continuous, normal– distribution–based Estimation–of–Distribution Algorithms (EDAs) by scaling the variance up from the maximum–likelihood estimate. When done properly, such scaling has been shown to prevent premature convergence on slope–like regions of the search space. In this paper we specifically focus on one way of scaling that was previously introduced as Adaptive Variance Scaling (AVS). It was found that when using AVS, the average number of fitness evaluations grows subquadratically with the dimensionality on a wide range of unimodal test–problems, competitively with the CMAES. Still, room for improvement exists because the variance doesn’t always have to be scaled. A previously introduced trigger based on correlation that determines when to apply scaling was shown to fail on higher dimensional problems. Here we provide a new solution called the Standard– Deviation Ratio (SDR) trigger that is integrated with the Iterate...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Peter A. N. Bosman, Jörn Grahl, Franz Rothlauf
• Keywords: Adaptive Variance, GECCO 2007, Normal– Distribution–based Estimation–of–distribution, Variance