A general framework for parallel distributed processing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Evolving neural networks through augmenting topologies
Evolutionary Computation
Proceedings of the European Conference on Genetic Programming
Modular Genetic Neural Networks for Six-Legged Locomotion
AE '95 Selected Papers from the European conference on Artificial Evolution
Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Symbiotic Multi-Robot Organisms: Reliability, Adaptability, Evolution
Symbiotic Multi-Robot Organisms: Reliability, Adaptability, Evolution
Coupled inverted pendulums: a benchmark for evolving decentral controllers in modular robotics
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Constraining connectivity to encourage modularity in HyperNEAT
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On the Performance of Indirect Encoding Across the Continuum of Regularity
IEEE Transactions on Evolutionary Computation
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This paper investigates the properties required to evolve Artificial Neural Networks for distributed control in modular robotics, which typically involves non-linear dynamics and complex interactions in the sensori-motor space. We investigate the relation between macro-scale properties (such as modularity and regularity) and micro-scale properties in Neural Network controllers. We show how neurons capable of multiplicative-like arithmetic operations may increase the performance of controllers in several ways whenever challenging control problems with non-linear dynamics are involved. This paper provides evidence that performance and robustness of evolved controllers can be improved by a combination of carefully chosen micro- and macro-scale neural network properties.