A GP approach for price-speed optimizing negotiation

This work uses a Genetic Programming (GP) algorithm to co-evolve negotiation strategies of agents that have different preference criteria, namely optimizing price and optimizing negotiation speed. While GP and other algorithms have been extensively used for price-only optimization, the problem of price-speed optimization has not yet received the same amount of attention. In Cloud/Grid computing environments, any delay in acquiring resources will be considered an overhead, hence negotiation agents need to adopt strategies that will enable them not only to optimize resource price but also to reach early agreements. This research is the earliest work to apply a GP algorithm for evolving price-speed optimizing negotiation strategies. An important advantage of the GP is its representation, which allows solutions to be represented in terms of the problem parameters, rather than as binary or real-value code, as it has been the case until now with other algorithms. We apply the GP to different negotiation scenarios and compare its results to other previously published works on the problem of pricespeed optimizing negotiation agents. Results show that the GP 1) outperforms the algorithms from these previous works and 2) can evolve to an optimal or near optimal strategy.