PSO as a meta-search for hyper-GA system to evolve optimal agendas for sequential multi-issue negotiation

This paper proposes a new technique based on Hyper Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) to evolve optimal agenda for bilateral multi-issue negotiation. In sequential negotiation the agenda specifies the set of issues included in the negotiation and the order in which they will be discussed. A player´s profit from negotiation depends on the agenda. Each player wants to find an agenda that yields the highest profit, i.e., his/her optimal agenda. Our proposed technique identifies the best set of issues to be included in the agenda as well as the best ordering for the issues in a way that increases the player´s profit. The proposed technique is comprised of two GA systems. Firstly, we have an outer GA system that searches for the best set of issues to be included in the agenda. Secondly, we have an inner GA system that searches for the best order of the selected issues. PSO is used to automatically adjust the parameters of these two GA systems. Empirical evidence demonstrates that the proposed technique evolves better agendas than standard GA, 1+1 Evolutionary Strategy, Fixed Settings Hyper-GA and a simple random search.