The employment of modern machine learning algorithms marks a huge advance towards automated and generalized segmentation in medical image analysis. Entire radiological datasets are classified, leading to a meaningful morphological interpretation, clearly distinguishing pathologies. After standard pre-processing, e.g. smoothing the input image data, the entire volume is partitioned into a large number of sub-regions utilizing watershed transform. These fragments are atomic and fused together building contiguous structures representing organs and typical morphology. This fusion is driven by similarity of regions. The relevant similarity measures respond to statistical a-priori models, derived from training datasets. In this work, the applicability of evolution strategy as classifier for a generic image segmentation approach is evaluated. Furthermore, it is analyzed if accuracy and robustness of the segmentation are improved by incorporation of meta features evaluated on the entire classification solution besides local features evaluated for the pre-fragmented regions to classify. The proposed generic strategy has a high potential in new segmentation domains, relying only on a small set of reference segmentations, as evaluated for different imaging modalities and diagnostic domains, such as brain MRI or abdominal CT. Comparison with results from other machine learning approaches, e.g. neural networks or genetic programming, proves that the newly developed evolution strategy is highly applicable for this classification domain and can best incorporate meta features for evaluation of solution fitness.