Abstract
This paper presents a hyper GA system to evolve optimal agendas for package deal negotiation. The proposed system uses a Surrogate Model based on Radial Basis Function Networks (RBFNs) to speed up the evolution. The negotiation scenario is as follows. There are two negotiators/agents (a and b) and m issues/items available for negotiation. But from these m issues, the agents must choose g < m issues and negotiate on them. The g issues thus chosen form the agenda. The agenda is important because the outcome of negotiation depends on it. Furthermore, a and b will, in general, get different utilities/profits from different agendas. Thus, for competitive negotiation (i.e., negotiation where each agent wants to maximize its own utility), each agent wants to choose an agenda that maximizes its own profit. However, the problem of determining an agent's optimal agenda is complex, as it requires combinatorial search. To overcome this problem, we present a hyper GA method that uses a Surrogate Model based on Radial Basis Function Networks (RBFNs) to find an agent's optimal agenda. The performance of the proposed method is evaluated experimentally. The results of these experiments demonstrate that the surrogate assisted algorithm, on average, performs better than standard GA and random search.