Optimization problem solving with auctions in Cultural Algorithms

In this paper we investigate the role that the competition among knowledge sources plays in the problem solving behavior of a population as the complexity of the optimization problem solving landscapes increases. We employ a type of game theoretic mechanism, auctions, in our study. Our main goal is to determine whether it matters if knowledge sources competed for individuals using auction mechanisms or weighted majority win mechanisms as the landscape complexity increased. The weighted majority win situation allows the Knowledge Sources to make predictions about future success whereas the Auction mechanism allows them to invest in their future by using tokens earned from recent performance. This latter approach allows contextual Knowledge Sources such as Situational Knowledge to play a larger role. Although the results are preliminary, it appears that the auction mechanism is more efficient when solving a problem in situations where contextual information is available. In that case, it is easier for the knowledge sources to make judicial bids or investments. However, once the landscapes become chaotic there is less contextual information available and correspondingly little advantage to the auction mechanism over the weighted majority win situation in terms of the number of generations needed to achieve a solution.