Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence

By John H. Holland. 1992

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “RL NNs can also be evolved through Evolutionary Algorithms (EAs) (Rechenberg, 1971; Schwefel, 1974; Holland, 1975; Fogel et al., 1966; Goldberg, 1989) in a series of trials.”
Deep Networks with Internal Selective Attention through Feedback Connections (2014, methodology): “The NES family of black-box optimization algorithms use parameterized probability distributions over the search space, instead of an explicit population (i.e., a conventional ES [33–35]).”
NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search (2018, background): “However, optimizing with N population members processes O ( N 3 ) sub-regions of the search space in parallel Goldberg (1989); Holland (1975).”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “1, except that C is now an FNN or RNN trained by evolutionary algorithms [200, 255, 105, 56, 68] applied to NNs [165, 321, 180, 259, 72, 90, 89, 110, 94], or by policy gradient methods [314, 315, 316, 274, 18, 1, 63, 128, 313, 210, 192, 191, 256, 85, 312, 190, 82, 93][245, Sec.”
Optimal Ordered Problem Solver (2002, neutral): “Unlike logicbased program synthesizers (Green, 1969, Waldinger and Lee, 1969, Deville and Lau, 1994), program evolvers use biology-inspired concepts of Evolutionary Computation (Rechenberg, 1971, Schwefel, 1974) or Genetic Algorithms (Holland, 1975) to evolve better and better computer programs.”
Community detection in graphs (2009, methodology): “Genetic algorithms (Holland, 1992) have also been used to optimize modularity.”
Self-Delimiting Neural Networks (2012, positive): “A more general approach for partially observable environments directly evolves programs for RNNs with internal states (no need for the Markovian assumption), by applying evolutionary algorithms [45, 68, 28] to RNN weight matrices [82, 70, 72, 25].”
A symbolic data-driven technique based on evolutionary polynomial regression (2006, methodology): “The global search for the best form of Equation (8) is performed by means of a standard GA (Holland 1975; Goldberg 1989).”
Tuning of the structure and parameters of neural network using an improved genetic algorithm (2001, methodology): “Owing to its particular structure, a neural network is very good in learning [2] using some learning algorithms such as GA [1] and back propagation algorithm [2].”
Learning in spiking neural networks (2013, background): “As evolution has been described in biology, similar processes can be also described in numerous other fields (Holland, 1975).”
Evolving artificial neural networks (1999, background): “The canonical genetic algorithm (GA) [13], [14] has always used binary strings to encode alternative solutions, often termed chromosomes.”