  Blotto Games are a popular model of multi-dimensional strategic resource
allocation. Two players allocate resources in different battlefields in an
auction setting. While competition with equal budgets is well understood,
little is known about strategic behavior under asymmetry of resources. We
introduce a genetic algorithm, a search heuristic inspired from biological
evolution, interpreted as social learning, to solve this problem. Most
performant strategies are combined to create more performant strategies.
Mutations allow the algorithm to efficiently scan the space of possible
strategies, and consider a wide diversity of deviations. We show that our
genetic algorithm converges to the analytical Nash equilibrium of the symmetric
Blotto game. We present the solution concept it provides for asymmetrical
Blotto games. It notably sees the emergence of "guerilla warfare" strategies,
consistent with empirical and experimental findings. The player with less
resources learns to concentrate its resources to compensate for the asymmetry
of competition. When players value battlefields heterogeneously, counter
strategies and bidding focus is obtained in equilibrium. These features are
consistent with empirical and experimental findings, and provide a learning
foundation for their existence.
