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Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in power at various prices. Because these markets are by nature repeated day after day, they are prone to tacit collusion between generators to raise prices. Tacit collusion occurs when a generator could improve its profits by changing its bid, but chooses not to do so because the long run result (as other generators also bid more aggressively) would be worse. A characteristic of such collusion is some type of punishment that occurs in the longer term if any player seeks a short-term advantage. It is hard to analyse such behaviour, partly because many possible (stable) collusive patterns exist. The approach taken here is to use a co-evolutionary genetic algorithm within a simple model of an electricity market to investigate the way that participants can learn collusive behaviour. We show that this framework does indeed lead to players adopting strategies of tacit collusion and we analyse the strategies that emerge. This also enables us to consider the ways in which the market environment increases or decreases the probability of collusion.