Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models
Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models
Double Auctions Across a Constrained Transmission Line
Operations Research
Smooth boosting and learning with malicious noise
The Journal of Machine Learning Research
Boosting in the presence of noise
Journal of Computer and System Sciences - Special issue: Learning theory 2003
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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.