A primary goal of evolutionary robotics (ER) is generalized control. That is, a robot controller should be capable of solving a variety of tasks in a domain, rather than only addressing specific instances of a task. Prior work has shown that Lexicase selection is more effective than other evolutionary algorithms for a wall crossing task domain where quadrupedal animats are evaluated on walls of varying height. In this work we expand baseline treatments in this task domain and examine specific aspects of the Lexicase selection algorithm across a variety of different parameter configurations. We identify the most effective Lexicase parameters for this task. Results indicate that Lexicase’s success is potentially due to maintaining population diversity at a higher level than other algorithms explored for this domain.

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