# The sensitivity of HyperNEAT to different geometric representations of a problem

@article{Clune2009TheSO, title={The sensitivity of HyperNEAT to different geometric representations of a problem}, author={Jeff Clune and Charles Ofria and Robert T. Pennock}, journal={Proceedings of the 11th Annual conference on Genetic and evolutionary computation}, year={2009} }

HyperNEAT, a generative encoding for evolving artificial neural networks (ANNs), has the unique and powerful ability to exploit the geometry of a problem (e.g., symmetries) by encoding ANNs as a function of a problem's geometry. This paper provides the first extensive analysis of the sensitivity of HyperNEAT to different geometric representations of a problem. Understanding how geometric representations affect the quality of evolved solutions should improve future designs of such…

## 58 Citations

### HyperNeat Plus the Connection Cost Technique

- Computer Science
- 2018

It is shown that adding the connection cost technique to Hyper NEAT produces neural networks that are significantly more modular, regular, and higher performing than HyperNEAT without a connection cost, even when compared to a variant of HyperNEat that was specifically designed to encourage modularity.

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This dissertation presents a new NE algorithm called Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT), based on a novel indirect encoding of ANNs, designed to work in tactical and strategic decision domains.

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### Evolving neural networks that are both modular and regular: HyperNEAT plus the connection cost technique

- Computer ScienceGECCO
- 2014

It is shown that adding the connection cost technique to Hyper NEAT produces neural networks that are significantly more modular, regular, and higher performing than HyperNEAT without a connection cost, even when compared to a variant of HyperNEat that was specifically designed to encourage modularity.

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The first documented case of HyperNEAT producing a modular phenotype is presented, but the inability to encourage modularity on harder problems where modularity would have been beneficial suggests that more work is needed to increase the likelihood that Hyper NEAT and similar algorithms produce modular ANNs in response to challenging, decomposable problems.

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- Computer Science
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positive results were achieved for non-trivial tasks, and some important characteristics of HyperNEAT not previously reported were discovered: a bias towards generating weight patterns aligned with the topographical arrangement of neurons within the layers of the substrate network; the ability to evolve solutions more quickly with larger substrate networks; and a possible difficulty co-evolving weight patterns for substrate networks containing more than one hidden layer.

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- BiologyNeural Computation
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This letter shows that when geometry is introduced to evolved ANNs through the hypercube-based neuroevolution of augmenting topologies algorithm, they begin to acquire characteristics that indeed are reminiscent of biological brains.

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This paper compares the performance of two classes of gait-learning algorithms: locally searching parameterized motion models and evolving artificial neural networks with the HyperNEAT generative encoding and a new method that builds a model of the fitness landscape with linear regression to guide further exploration.

### Evolving Gaits for Physical Robots with the HyperNEAT Generative Encoding: The Benefits of Simulation

- Computer Science, EngineeringEvoApplications
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This paper tested the hypothesis that the beneficial properties of Hyper NEAT would outperform the simpler encoding if HyperNEAT gaits are first evolved in simulation before being transferred to reality, and it was confirmed, resulting in the fastest gaits yet observed for this robot.

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- Computer Science2017 IEEE Symposium Series on Computational Intelligence (SSCI)
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Results demonstrate that although HyperNEAT was not able to achieve as robust results as a hand-design approach, the best strategy was comparable, with just a 3–4% drop in performance.

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