Automatic definition of modular neural networks
Adaptive Behavior
“Genotypes” for neural networks
The handbook of brain theory and neural networks
Evolving neural networks through augmenting topologies
Evolutionary Computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Acquiring evolvability through adaptive representations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary reinforcement learning of artificial neural networks
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
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
A case study on the critical role of geometric regularity in machine learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Darwinian embodied evolution of the learning ability for survival
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Neuroevolution with analog genetic encoding
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Evolutionary Development of Hierarchical Learning Structures
IEEE Transactions on Evolutionary Computation
Analog Genetic Encoding for the Evolution of Circuits and Networks
IEEE Transactions on Evolutionary Computation
The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming
IEEE Transactions on Evolutionary Computation
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The framework of neuroevolution (NE) provides a way of finding appropriate structures as well as connection weights of artificial neural networks. However, the conventional NE approach of directly coding each connection weight by a gene is severely limited in its scalability and evolvability. In this study, we propose a novel indirect coding approach in which a phenotypical network develops from the genes encoding multiple subnetwork modules. Each gene encodes a subnetwork consisting of the input, hidden, and output nodes and connections between them. A connection can be a real weight or a pointer to another subnetwork. The structure of the network evolves by inserting new connection weights or subnetworks, merging two subnetworks as a higherlevel subnetwork, or changing the existing connections. We investigated the evolutionary process of the network structure using the task of double pole balancing. We confirmed that the proposed method by the modular developmental rule can produce a wide variety of network architectures and that evolution can trim them down to the most appropriate ones required by the task.