An adaptive genetic algorithm and its application in bilateral multi-issue negotiation

In multi-agent based E-commerce, how to make the negotiation agents gain satisfying result farthest and negotiate efficiently is a key issue. As for this problem, an adaptive genetic algorithm is presented and the algorithm is applied in bilateral multi-issue simultaneous bidding negotiation. In the system, agents can send their information including issue • issue weight • issue reservation price etc to a third party agent, and the third party agent then uses the adaptive genetic algorithm to give the optimal solution. In the experiments, the two methods are used to compare. One is the simple genetic algorithm (SGA); the second is the adaptive genetic algorithm (AGA). The SGA uses 218 runs to gain the satisfying result, while the AGA only uses 152 runs to gain the satisfying result. The experiments' results show that agents with AGA can negotiation more efficiently than SGA in multi-agent based e-commerce.