Abstract
To test the performance of a neuroevolutionary system after adding a new feature, or in general when trying to assess its abilities, it is important to have some standardized benchmarking problems. In this chapter we create two such benchmarking problems, the Pole Balancing Benchmarks (Single, Double, and With and Without dampening), and the T-Maze navigation benchmark, which is one of the problems used to assess the performance of recurrent and plasticity enabled neural network based systems.
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Sher, G.I. (2013). Creating the Two Slightly More Complex Benchmarks. In: Handbook of Neuroevolution Through Erlang. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4463-3_14
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DOI: https://doi.org/10.1007/978-1-4614-4463-3_14
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