Electronic Noses (ENs) might represent a simple, fast, high sample throughput and economic alternative to conventional analytical instruments . However, gas sensors drift still limits the EN adoption in real industrial setups due to high recalibration effort and cost . In fact, pattern recognition (PaRC) models built in the training phase become useless after a period of time, in some cases a few weeks. Although algorithms to mitigate the drift date back to the early 90 this is still a challenging issue for the chemical sensor community. Among other approaches, adaptive drift correction methods adjust the PaRC model in parallel with data acquisition without need of periodic calibration. Self‐Organizing Maps (SOMs) and Adaptive Resonance Theory (ART) networks have been already tested in the past with fair success. This paper presents and discusses an original methodology based on a Covariance Matrix Adaptation Evolution Strategy (CMA‐ES), suited for stochastic optimization of complex problems.