Dangerousness Metric for Gene Regulated Car Driving

13 Apr

Paper presented as interactive talk and poster at EvoGAMES 2016 in Porto (Portugal).

Sylvain Cussat-Blanc, Jean Disset, Stephane Sanchez


In this paper, we show how a dangerousness metric can be used to modify the input of a gene regulatory network when plugged to a virtual car. In the context of the 2015 Simulated Car Racing Championship organized during GECCO 2015, we have developed a new cartography methodology able to inform the controller of the car about the incoming complexity of the track: turns (slipperiness, angle, etc.) and bumps. We show how this dangerousness metric improves the results of our controller and outperforms other approaches on the tracks used in the competition.


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