EvoNash: Accelerating Convergence to Nash Equilibrium
CWSF · 2026 Digital Technology Silver Medal
Overview
EvoNash: Accelerating Convergence to Nash Equilibrium Investigating Adaptive Mutation Rates in Genetic Neural Networks This experiment investigates the efficiency of evolutionary algorithms in finding Nash Equilibrium in a competitive multi-agent environment. I compare a standard static mutation rate against a novel adaptive mutation strategy where the mutation rate scales inversely with an agent's fitness score. The hypothesis is that adaptive mutation, mimicking biological 'stress-induced mutagenesis', will allow low-fitness populations to explore the solution space aggressively while high-fitness populations retain their successful strategies, resulting in significantly faster convergence to a stable strategy (Nash Equilibrium).
Awards (2)
- Silver Medal
- Selected for CWSF 2026
Competition history
- CWSF 2026
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