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About

Andreas Steyven’s research lies at the intersection of swarm robotics, evolutionary computation, and collective intelligence, with a focus on designing algorithms that enable robot swarms to adapt, diversify, and survive autonomously. His major contributions include pioneering the first decentralised quality-diversity algorithm for evolving functionally diverse swarms, which demonstrated that groups can achieve greater robustness and problem-solving ability without centralised control—a key insight for real-world swarm applications. This work, his most cited (18 citations), has influenced subsequent research in evolutionary robotics and collective behaviour. Steyven also advanced environment-driven evolutionary algorithms, notably extending the mEDEA framework to incorporate explicit relative fitness and fitness-proportionate communication, improving swarm survivability over long periods (9 citations). In a notable study on communication costs (4 citations), he showed that penalising communication implicitly selects for energy-efficient genomes, linking evolutionary pressure to practical resource constraints. His work on open-ended evolution (2 citations) further explores how environmental factors shape adaptive dynamics. Steyven’s contributions are essential reading for researchers seeking to build resilient, scalable, and functionally diverse robot swarms that operate under real-world constraints.