Using Marker-Based Genetic Encoding Of Neural Networks To Evolve Finite-State Behaviour

By Brad Fullmer and Risto Miikkulainen. 1991

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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 [2, 9, 18, 26, 34, 43, 58, 64, 76, 77, 87, 92, 116, 124, 126].”
Evolving Neural Networks through Augmenting Topologies (2002, methodology): “Many systems have been developed over the last decade that evolve both neural network topologies and weights (Angeline et al., 1993; Braun and Weisbrod, 1993; Dasgupta and McGregor, 1992; Fullmer and Miikkulainen, 1992; Gruau et al., 1996; Krishnan and Ciesielski, 1994; Lee and Kim, 1996; Mandischer”
Evolving a Roving Eye for Go (2004, background): “Fullmer and Miikkulainen evolved simple " creatures " that move around a shape on a grid and must learn to move onto only certain shapes.”
Meta learning evolutionary artificial neural networks (2004, background): “tial arrangement and their efferent connectivity. Several high level coding schemes like graph generation system [49], Symbiotic Adaptive Neuro-Evolution (SANE) [62] [65], marker based genetic coding [36], L-systems [13], cellular encoding [38], fractal representation [58] etc are some of the rugged”
Meta learning evolutionary arti ! cial neural networks (2004, background): “Several high level coding schemes like graph generation system [49], symbiotic adaptive neuro-evolution (SANE) [62,65], marker based genetic coding [36], L-systems [13], cellular encoding [38], fractal representation [58], etc.”