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About

Antonios Liapis is a computational intelligence researcher whose work sits at the intersection of evolutionary computation, robotics, and computational creativity. He is best known for pioneering contributions to divergent search methodologies — algorithms designed to overcome the limitations of traditional evolutionary approaches that tend to converge prematurely on a single solution. His most influential work introduced **surprise search** (2016, 41 citations), a novel evolutionary paradigm inspired by the principle of surprise, which drives discovery of unconventional solutions in deceptive problem spaces, validated across multiple robot navigation tasks. Building on this foundation, Liapis has systematically expanded the divergent search toolkit, exploring how novelty and surprise can be fused or coupled to enhance evolutionary diversity, including applications to soft robot morphology evolution and multi-behavior variants of the MAP-Elites quality-diversity algorithm. His research demonstrates consistent engagement with a central challenge in the field: enabling algorithms to explore broadly rather than exploit narrowly. Across his cited body of work, Liapis has helped establish divergent search as a legitimate and powerful alternative to objective-driven optimization, offering meaningful tools for researchers working in autonomous robotics, generative systems, and creative AI.