• Corpus ID: 4687964

HyperNeat Plus the Connection Cost Technique

@inproceedings{Huizinga2018HyperNeatPT,
  title={HyperNeat Plus the Connection Cost Technique},
  author={Joost Huizinga and Jean-Baptiste Mouret},
  year={2018}
}
One of humanity’s grand scientific challenges is to create artificially intelligent robots that rival natural animals in intelligence and agility. A key enabler of such animal complexity is the fact that animal brains are structurally organized in that they exhibit modularity and regularity, amongst other attributes. Modularity is the localization of function within an encapsulated unit. Regularity refers to the compressibility of the information describing a structure, and typically involves… 

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References

SHOWING 1-10 OF 38 REFERENCES

Investigating whether hyperNEAT produces modular neural networks

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.

Constraining connectivity to encourage modularity in HyperNEAT

This paper investigates how altering the traditional approach to determining whether connections are expressed in HyperNEAT influences modularity, and provides an important clue to how an indirect encoding of network structure can be encouraged to evolve modularity.

A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks

The main conclusion is that the ability to explore the space of regular connectivity patterns opens up a new class of complex high-dimensional tasks to neuroevolution.

Evolving coordinated quadruped gaits with the HyperNEAT generative encoding

It is demonstrated that HyperNEAT, a new and promising generative encoding for evolving neural networks, can evolve quadruped gaits without an engineer manually decomposing the problem.

Generating large-scale neural networks through discovering geometric regularities

A method, called Hypercube-based Neuroevolution of Augmenting Topologies (HyperNEAT), which evolves a novel generative encoding called connective Compositional Pattern Producing Networks (connective CPPNs) to discover geometric regularities in the task domain, allowing the solution to both generalize and scale without loss of function to an ANN of over eight million connections.

On the Performance of Indirect Encoding Across the Continuum of Regularity

This paper presents the first comprehensive study showing that phenotypic regularity enables an indirect encoding to outperform direct encoding controls as problem regularity increases, and suggests a path forward that combines indirect encodings with a separate process of refinement.

On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks

The results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks, and reveal the consequence of the bias of developmental encodings towards regular structures.

Evolving scalable and modular adaptive networks with Developmental Symbolic Encoding

A novel developmental encoding for networks, featuring scalability, modularity, regularity and hierarchy is proposed, which allows to represent structural regularities of networks and build them from encapsulated and possibly reused subnetworks.

Evolving Neural Networks through Augmenting Topologies

A method is presented, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task and shows how it is possible for evolution to both optimize and complexify solutions simultaneously.

Evolving the placement and density of neurons in the hyperneat substrate

An extension called evolvable-substrate HyperNEAT (ES-HyperNEAT) is introduced that determines the placement and density of the hidden nodes based on a quadtree-like decomposition of the hypercube of weights and a novel insight about the relationship between connectivity and node placement.