Since the inception of learning classifier systems (LCS) by John Holland, learning paradigms driven by genetic algorithms (GA) have shown their competence on a broad spectrum of fields and applications. In a broader spectrum, genetics-based machine learning (GBML) systems have successfully tackled the creation of cognitive models, classification and prediction systems, and anticipatory behavior—to mention a few. This group focuses on collaborating to advance and developed such techniques.