A GP approach for price-speed optimizing negotiation

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