A Model for the Dynamic Interaction Between Evolution and Learning
Neural Processing Letters
Evolving neural networks through augmenting topologies
Evolutionary Computation
A Taxonomy for artificial embryogeny
Artificial Life
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
Neural Networks in a Softcomputing Framework
Neural Networks in a Softcomputing Framework
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
A novel generative encoding for exploiting neural network sensor and output geometry
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Generating large-scale neural networks through discovering geometric regularities
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Acquiring evolvability through adaptive representations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Adding learning to the cellular development of neural networks: Evolution and the baldwin effect
Evolutionary Computation
Generative encoding for multiagent learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
How a Generative Encoding Fares as Problem-Regularity Decreases
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
The sensitivity of HyperNEAT to different geometric representations of a problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
HyperNEAT controlled robots learn how to drive on roads in simulated environment
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
MBEANN: mutation-based evolving artificial neural networks
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Revising the evolutionary computation abstraction: minimal criteria novelty search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Transfer learning through indirect encoding
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving the placement and density of neurons in the hyperneat substrate
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving neural networks in compressed weight space
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Investigating whether hyperNEAT produces modular neural networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
NEATfields: evolution of neural fields
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Indirectly encoding neural plasticity as a pattern of local rules
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Evolving a single scalable controller for an octopus arm with a variable number of segments
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
HybrID: a hybridization of indirect and direct encodings for evolutionary computation
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Evolution of multisensory integration in large neural fields
EA'11 Proceedings of the 10th international conference on Artificial Evolution
Evolution of multisensory integration in large neural fields
EA'11 Proceedings of the 10th international conference on Artificial Evolution
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We have developed an extension of the NEAT neuroevolution method, called NEATfields, to solve problems with large input and output spaces. The NEATfields method is a multilevel neuroevolution method using externally specified design patterns. Its networks have three levels of architecture. The highest level is a NEAT-like network of neural fields. The intermediate level is a field of identical subnetworks, called field elements, with a two-dimensional topology. The lowest level is a NEAT-like subnetwork of neurons. The topology and connection weights of these networks are evolved with methods derived from the NEAT method. Evolution is provided with further design patterns to enable information flow between field elements, to dehomogenize neural fields, and to enable detection of local features. We show that the NEATfields method can solve a number of high dimensional pattern recognition and control problems, provide conceptual and empirical comparison with the state of the art HyperNEAT method, and evaluate the benefits of different design patterns.