Anti-Hebbian plasticity outperforms Hebbian in morphogenetically grown recurrent controllers, with co-evolution independently recovering effective plasticity settings and showing higher topology dependence than random networks.
Proceedings of the Genetic and Evolutionary Computation Conference Companion , pages =
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Post-selection with DL or FBF after multi-objective GP search improves test-set performance over AIC/BIC baselines on noisy synthetic and real regression tasks, while using DL directly as fitness often causes premature convergence to overly simple models.
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Activity-Dependent Plasticity in Morphogenetically-Grown Recurrent Networks
Anti-Hebbian plasticity outperforms Hebbian in morphogenetically grown recurrent controllers, with co-evolution independently recovering effective plasticity settings and showing higher topology dependence than random networks.
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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions
Post-selection with DL or FBF after multi-objective GP search improves test-set performance over AIC/BIC baselines on noisy synthetic and real regression tasks, while using DL directly as fitness often causes premature convergence to overly simple models.