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Behavioural Plasticity Can Help Evolving Agents in Dynamic Environments but at the Cost of Volatility.

, , , , , and . ACM Trans. Auton. Adapt. Syst., 15 (4): 11:1-11:26 (2020)

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The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities, , , , , , , , , and 43 other author(s). (2018)cite arxiv:1803.03453.Reward tampering and evolutionary computation: a study of concrete AI-safety problems using evolutionary algorithms., , and . Genet. Program. Evolvable Mach., 24 (2): 12 (December 2023)Open-Ended Search for Environments and Adapted Agents Using MAP-Elites., , and . EvoApplications, volume 13224 of Lecture Notes in Computer Science, page 651-666. Springer, (2022)Coevolutionary Learning of Neuromodulated Controllers for Multi-Stage and Gamified Tasks., , , , , and . ACSOS, page 129-138. IEEE, (2020)Different regulation mechanisms for evolved critical periods in learning.. IEEE Congress on Evolutionary Computation, page 1193-1200. IEEE, (2013)Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills., , and . PLoS Comput. Biol., (2015)Planning Inspection Paths through Evolutionary Multi-objective Optimization., , and . GECCO, page 893-900. ACM, (2016)How do Mixture Density RNNs Predict the Future?, , and . CoRR, (2019)Evolved Sensitive Periods in Learning.. ECAL, page 409-416. MIT Press, (2013)RADAR: Reactive and Deliberative Adaptive Reasoning - Learning When to Think Fast and When to Think Slow., , , , , and . ICDL, page 184-189. IEEE, (2022)