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cs.NE2026
Provably Sub-Linear Two-Timescale NeuroEvolution with Online Plasticity
Shishen Lin, Yixin Chen
NeuroEvolution of Augmenting Topologies (NEAT) is a widely used neuroevolution algorithm for learning neural network architectures and weights for control tasks. However, standard…
cs.NE2024
Overcoming Binary Adversarial Optimisation with Competitive Coevolution
Per Kristian Lehre, Shishen Lin
Co-evolutionary algorithms (CoEAs), which pair candidate designs with test cases, are frequently used in adversarial optimisation, particularly for binary test-based problems where…
cs.NE2024
Concentration Tail-Bound Analysis of Coevolutionary and Bandit Learning Algorithms
Per Kristian Lehre, Shishen Lin
Runtime analysis, as a branch of the theory of AI, studies how the number of iterations algorithms take before finding a solution (its runtime) depends on the design of the algorit…