… Neural networks are parallel processing structures that provide the capability to perform various
pattern recognition tasks. A network … for training a general neural network. This approach …

… evolution are two fundamental forms of adaptation. There has been a great interest in
combining learning and evolution with artificial neural networks (… EA’s to evolve ANN connection …

… to gain an advantage from evolving neural network topologies along with weights. … evolution
to both optimize and complexify solutions simultaneously, offering the possibility of evolving …

… CoSyNE evolve partial neural networks and combine them into fully functional networks [[22]…
[15] have been developed to evolve the topology of the neural network, which is particularly …

… evolving artificial neural networks (ANN’s). The evolutionary algorithm used in EPNet is
based on Fogel’s evolution… various aspects of evolutionary artificial neural networks (EANN’s). …

Neuroevolution, ie evolution of artificial neural networks, has recently emerged as a powerful
… methods that evolve fixed-topology networks, network topologies, and network construction …

… Abstract—The success of Deep Neural Networks (DNNs) for … start with (similar to artificial
neural networks). If the topology is … prospect for evolving optimized deep neural networks which …

… evolution as a mechanism for learning to play go. The SANE (Symbiotic, Adaptive Neuro-Evolution
[7, 8, 9]) algorithm demonstrates that networks … go and why neural network techniques …

… To evolve a cascade network with a CCGA we decompose the network into substructures
and map the substructures into species. The decomposition we have chosen corresponds to …

… , one must take into consideration the role of evolution, which is the primary adaptive process.
… algorithm to simulate the evolution of a population of neural networks, each controlling the …