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Published in:
Introduction: Applications & Motivations Read chapter Read first chapter

2013 | OriginalPaper | Chapter

14. Creating the Two Slightly More Complex Benchmarks

Author : Gene I. Sher

Published in: Handbook of Neuroevolution Through Erlang

Publisher: Springer New York

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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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previous chapter The Benchmarker
next chapter Neural Plasticity
Literature
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Gomez F, Schmidhuber J, Miikkulainen R (2008) Accelerated Neural Evolution through Cooperatively Coevolved Synapses. Journal of Machine Learning Research 9, 937-965.MATHMathSciNet
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Durr P, Mattiussi C, Soltoggio A, Floreano D (2008) Evolvability of Neuromodulated Learning for Robots. 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems LABRS, 41-46.CrossRef
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Blynel J, Floreano D (2003) Exploring the T-maze: Evolving Learning-Like Robot Behaviors using CTRNNs. Applications of evolutionary computing 2611, 173-176.
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Risi S, Stanley KO (2010) Indirectly Encoding Neural Plasticity as a Pattern of Local Rules. Neural Plasticity 6226, 1-11.
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Soltoggio A, Bullinaria JA, Mattiussi C, Durr P, Floreano D (2008) Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios. Artificial Life 2, 569-576.
Metadata
Title
Creating the Two Slightly More Complex Benchmarks
Author
Gene I. Sher
Copyright Year
2013
Publisher
Springer New York
Book
Handbook of Neuroevolution Through Erlang

Print ISBN: 978-1-4614-4462-6

Electronic ISBN: 978-1-4614-4463-3

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4614-4463-3_14

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