An evolutionary random search algorithm for double auction markets

An evolutionary random search algorithm is proposed for learning of the optimum bid in double auction markets where the agents are either members of the population of sellers or the population of buyers. Sellers and buyers are attempting to learn their optimum bid or offer prices, respectively, that maximize their individual gain in the next round of the game. The performance of the algorithm presented in this paper is compared with the performance of the genetic learning algorithm previously used for the same purpose. Multiple simulations demonstrate that the new algorithm converges faster to a market equilibrium. Learning in the presence of uncertainties is also studied.