Comparing crossover operators in Neuro-Evolution with crowd simulations

Crowd simulations are a set techniques used to control groups of agents and are exemplified by scenes from movies such as The Lord of the Rings and Inception. A problem which all crowd simulation techniques suffer from is the balance between control of the crowd behaviour and the autonomy of the agents. One possible solution to this problem is to use Neuro-Evolution (NE) to evolve the agent models so that the agents behave realistically and the emergent crowd behaviour is controllable. Since this is not an application area which has been investigated much, it is unknown which NE parameters and operators work well. This paper attempts to address this by comparing the performance of a set of crossover operators with a range of probabilities in three simulations: Car Racing, Mouse Bridge Crossing, and a War-Robot Battle. Overall it was found that Laplace crossover worked the best across all our simulations.