When learning guides evolution

By John Maynard Smith. 1987

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Citations in the corpus

Weight Agnostic Neural Networks (2019, methodology): “In our approach, we evaluate architectures without weight training, bypassing the costly inner loop, similar to the random trial approach in [44, 99] that evolved architectures to be more weight tolerant.”
Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “The studies in Smith (1986); Hinton and Nowlan (1987); Boers et al. (1995); Mayley (1996); Bullinaria (2001) demonstrated the Baldwin effect with computational simulations.”
Auto-teaching: networks that develop their own teaching input (1993, methodology): “This implies that evolution progressively selects networks that internally generate teaching input which can be used by the networks themselves to learn during their life how to search for food efficiently.”
Evolving artificial neural networks (1999, background): “Various models have been proposed [257]–[271], but most of them deal with the issue of how learning can guide evolution [257]–[260] and the relationship between the evolution of architectures and that of connection weights [261]–[263].”
Learning to Adapt to Changing Environments in Evolving Neural Networks (1996, background): “More recently, Hinton and Nowlan (1987) have provided a clear example of how learning can guide evolution using a simulation model.”
Evolution of Reinforcement Learning in Uncertain Environments: A Simple Explanation for Complex Foraging Behaviors (2002, background): “Much of this research has been directed toward elucidating the relationship between evolving traits (such as synaptic weights) versus learning them (e.g. Ackley & Littman, 1991; Hinton & Nowlan, 1987).”
The Evolution of Learning: An Experiment in Genetic Connectionism (1991, background): “This is where the distinction between synchronic and diachronic emergence becomes important.”
Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm (1994, background): “This phenomenon (and other phenomena that result from this form of hybrid) is known as the Baldwin effect (Baldwin, 1896; Morgan, 1896; Waddington, 1942; Maynard Smith, 1987; Hinton & Nowlan, 1987; Ackley & Littman, 1991; Whitley & Gruau, 1993; Whitley et al., 1994; Anderson, in press ).”
Guiding or Hiding: Explorations into the Effects of Learning on the Rate of Evolution (1997, background): “It can be said that learning is`guiding' evolution (Hinton & Nowlan, 1987; Maynard Smith, 1987).”
Modelling brain emergent behaviours through coevolution of neural agents (2006, background): “The well known since 1896 Baldwin effect, discusses the outcome of epigenetic learning on evolution, with the best able to learn organisms having larger numbers of offsprings (Smith, 1987).”
Influence of Plasticity and Learning on Evolution under Directional Selection (2007, methodology): “Using a multilocus simulation model, Hinton and Nowlan (1987; see also Maynard Smith 1987) were the first to show that plasticity may accelerate evolutionary change.”
Evolution and Learning in an Intrinsically Motivated Reinforcement Learning Robot (2007, background): “This approach already highlighted several important aspects of the relationship (for a review, see [3]), for example the fact that learning can guide evolutionary search [4] and that evolution can discover good starting conditions which can in turn facilitate learning processes during lifetime [5].”