An Enhanced Hypercube-Based Encoding for Evolving the Placement, Density, and Connectivity of Neurons

@article{Risi2012AnEH,
  title={An Enhanced Hypercube-Based Encoding for Evolving the Placement, Density, and Connectivity of Neurons},
  author={Sebastian Risi and Kenneth O. Stanley},
  journal={Artificial Life},
  year={2012},
  volume={18},
  pages={331-363}
}
Intelligence in nature is the product of living brains, which are themselves the product of natural evolution. [] Key Result The main conclusion is that ES-HyperNEAT significantly expands the scope of neural structures that evolution can discover.
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66 Citations
Highly Influential Citations
6
Background Citations
27
Methods Citations
24
Results Citations
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66 Citations

HyperNEAT: The First Five Years

This chapter reviews these first 5 years of research that builds upon this approach, and culminates with thoughts on promising future directions.

Deep HyperNEAT : Evolving the Size and Depth of the Substrate Evolutionary

This report describes DeepHyperNEAT, an extension of HyperNEAT to allow it to alter the topology of its indirectlyencoded neural network so that it can continue to grow and increase in complexity over evolution.

Guided self-organization in indirectly encoded and evolving topographic maps

It is shown for the first time that the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) method can be seeded to begin evolution with such lateral connectivity, enabling genuine self-organizing dynamics.

On the automated, evolutionary design of neural networks: past, present, and future

This work aims to provide a complete reference of all works related to neuroevolution of convolutional neural networks up to the date, and to the best of its knowledge, this is the best survey reviewing the literature in this field.

Evolving Artificial Neural Networks through L-system and evolutionary computation

A biologically inspired Neuro Evolutive Algorithm able to generate modular, hierarchical and recurrent neural structures as those often found in the nervous system of live beings, and that enable them to solve intricate survival problems is presented.

Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture

The goal of this paper is to use evolutionary approaches to find suitable computational functions that are analogous to natural sub-components of biological neurons and demonstrate that intelligent behavior can be produced as a result of this additional biological plausibility.

ARTIFICIAL DEVELOPMENT AND EVOLUTION OF ARTIFICIAL NEURAL NETWORKS USING PARAMETRIC L-SYSTEMS WITH MEMORY

This research develops a biologically inspired methodology for automatic design of ANNs using an artificial development system based on a parametric Lindenmayer with memory integrated to a Genetic Algorithm (GA) which simulates artificial evolution, allowing generate architectures ofANNs direct and recurrent with optimal number of neurons and appropriate topology.

Using Indirect Encoding of Multiple Brains to Produce Multimodal Behavior

Novel multimodal extensions to HyperNEAT, a popular indirect encoding for evolving many brains without assuming geometric relationships between them, are introduced and it is shown that multi-brain approaches are more effective than Hyper NEAT without multimodAL extensions, and that brains without a geometric relation to each other outperform situational policy geometry.

A hybrid neuro-evolutive algorithm for neural network optimization

This paper proposes a hybrid neuro-evolutive algorithm (NEA) that uses a compact indirect encoding scheme (IES) for representing its genotypes, moreover has the ability to reuse the genotypes and…

A NEUROGENETIC ALGORITHM BASED ON RATIONAL AGENTS Ĺıdio

A biologically inspired NEA that evolves ANNs using these ideas as computational design techniques and the result is an optimized neural network architecture for solving classification problems.
...

References

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