David Shorten

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About

David Shorten is a researcher in evolutionary computation and artificial life, with a focus on understanding the dynamics of open-ended evolution and the mechanisms that drive algorithmic creativity. His work critically examines how different search paradigms—particularly novelty search and objective-based optimization—shape the evolvability and adaptability of populations. In his most cited paper, "How evolvable is novelty search?" (2014, 9 citations), Shorten systematically compared novelty-driven and objective-driven evolution in a maze-solving task, demonstrating that novelty search can produce populations with greater behavioral diversity and long-term adaptive potential. This contribution challenged conventional assumptions about fitness-based selection and highlighted the importance of exploration in evolutionary algorithms. His follow-up work, "Evolving Generalised Maze Solvers" (2015, 5 citations), extended these insights by investigating how evolved controllers can generalize across varied environments. Shorten’s research is valuable for students and researchers interested in evolutionary robotics, neuroevolution, and the theoretical foundations of search algorithms. His findings offer practical guidance for designing more robust and creative artificial evolutionary systems.