Evolutionary ordered neural network with a linked-list encoding scheme

By Chi-Ho Lee, Jong-Hwan Kim. 1996

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Citations in the corpus

Weight Agnostic Neural Networks (2019, background): “Our work has connections to existing work not only in deep learning, but also to various other ﬁelds: Architecture Search Search algorithms for neural network topologies originated from the ﬁeld of evolutionary computing in the 1990s [1, 8, 17, 25, 33, 40, 53, 59, 70, 71, 81, 86, 116, 117].”
Evolution of neural networks using Cartesian Genetic Programming (2010, background): “Direct encoding are generally thought of as better at fine tuning and generating compact architecture [1], [12], [25], [3], [19], [26], [14].”
Real-time neuroevolution in the NERO video game (2005, background): “Gruau et al. 1996; Hornby and Pollack 2002; Krishnan and Ciesielski 1994; Lee and Kim 1996; Mandischer”
Evolving Neural Networks through Augmenting Topologies (2002, methodology): “These methods encompass a range of ideas about how Topology and Weight Evolving Artificial Neural Networks (TWEANNs) should be implemented.”
Evolving Artiicial Neural Networks (2007, background): “Some researchers thus avoided crossover and adopted only mutations in the evolution of architectures [45, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 149, 179, 217, 223, 128] although it has been shown that crossover may be useful and important in increasing the e ciency of evol”
Evolving artificial neural networks (1999, background): “There are some other different EP-based systems for designing ANN’s [128], [129], [217], [223], but none has been tested on as many different benchmark problems.”