This paper describes the use of a genetic algorithm (GA) to find parameter-values for trading agents that operate in virtual "e-marketplaces", where the rules of the marketplaces are also under simultaneous control of the GA. The aim is to use the GA to automatically design new agent-based e-marketplaces that are more efficient than markets designed by (or populated by) humans. Das et al. (2001) recently demonstrated that ZIP software-agent traders consistently outperform human traders in Continuous Double Auction (CDA) marketplaces. Cliff (2001b) used a GA to explore a continuous space of auction mechanisms, with ZIP traders simultaneously evolving to operate efficiently in these evolved markets. The space of possible auction-types explored includes the CDA and also two purely one-sided mechanisms. Surprisingly, the GA did not settle on the CDA. Instead, in two experiments, optima were found at a one-sided auction mechanism; and in a third experiment a novel hybrid auction mechanism partway between the CDA and a one-sided auction was evolved. This paper extends that research by studying the auction mechanisms that evolve when the market supply and demand schedules undergo a sudden "shock" change half- way through the evaluation process. It is shown that hybrid market mechanisms (again partway between the CDA and a one-sided mechanism) can evolve in place of the one-sided solutions that evolve when there are no market shocks. Furthermore it is demonstrated that the precise nature of the hybrid auction that evolves is dependent on the nature of the shock. These results indicate that the evolution of one-sided mechanisms reported by Cliff (2001b) is an artefact of using single fixed schedules, and that in general two-sided auctions will evolve. These two-sided auctions may be hybrids unlike any human-designed auction and yet may also be significantly more efficient than any human designed market mechanism. 8 Pages
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