Gaussian Adaptation with Decaying Matrix Adaptation Weights

Gaussian Adaptation (GaA) is a black box op-timization algorithm that shares a similar evolution process with the well-studied Simulated Annealing (SA) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES). To improve the convergence properties of GaA in optimization, this paper presents a variant of GaA that degenerates during its evolution process into algorithms with well-proven convergence properties. Specifically, the proposed GaA variant termed A
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GaA employs an adaptive weighting of the covariance matrix update of the standard GaA. Consequently, as the evolution process progresses the algorithm degenerates into a variant of SA or (1+1)-CMA-ES. The performance of the proposed A
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GaA is evaluated on 30 functions from the IEEE 2014 benchmark suite and compared favorably with the original GaA as well as 6 other leading algorithms.