Representation and Evolution of Neural Networks

By M. Mandischer. 1993

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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].”
Evolving Neural Networks through Augmenting Topologies (2002, background): “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”
Evolutionary Algorithms for Neural Network Design and Training (1995, background): “Basically there are two general paradigms to design a representation of a network's structure: the strong, direct or low-level encoding [51, 66, 72] and the weak, indirect or high-level encoding [29, 28, 38, 44].”
Accelerated Neural Evolution through Cooperatively Coevolved Synapses (2008, background): “Some simply provide a coarse description such as delineating a neuron's receptive field (Mandischer, 1993) or connective density (Harp et al., 1989), while others are more algorithmic, providing growth rules in the form of graph generating grammars (Kitano, 1990; Voigt et al., 1993; Gruau et al.,…”
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm (2017, methodology): “Examples include different encoding methods [17], simultaneous weight and architecture evolution [18], and algorithmic modification and redesign [19–24].”