Landscapes, Learning Costs, and Genetic Assimilation

By Giles Mayley. 1996

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Citations in the corpus

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.”
Embodied intelligence via learning and evolution (2021, background): “Thus we actually discover a stronger form of the Baldwin effect than has been previously conjectured in the literature 38,39 by demonstrating that an explicit selection pressure for the speed of skill acquisition is not necessary for the Baldwin effect to hold.”
Evolutionary robotics: coping with environmental change (2000, methodology): “A limitation of this approach is the \Baldwin eeect", whereby the evolutionary costs associated with learning give a selective advantage to the genetic assimilation of learned properties and consequently reduce the plasticity of the system over time (Mayley, 1996).”
Interactions between Learning and Evolution : The Outstanding Strategy Generated by the Baldwin E ect (2000, methodology): “Also, Mayley explored two criteria for the second step of the Baldwin e ect by using NK tness landscapes (Mayley, 1997).”
Learning and Evolution in Neural Networks (1994, background): “Nolfi and Parisi (1997), evolved neural controllers for a small mobile robot that was asked to explore an arena of 60 x 20 cm surrounded by walls.”
Guiding or Hiding: Explorations into the Effects of Learning on the Rate of Evolution (1997, background): “Another condition that has been discussed in (Mayley, 1996b) is that the genotypic space and the phenotypic space must have the property of neighbourhood correlation with respect to the genetic operators and the learning rule for genetic assimilation of a learned trait to be guaranteed.”
Neuroevolution: from architectures to learning (2008, background): “3 By systematically varying the cost of learning and the correlation between the learning space and the evolutionary space, Mayley [47] showed that: (1) the adaptive advantage of learning is proportional to the correlation between the two search spaces; (2) the assimilation of characters first acqui”
Neuronal Plasticity and Temporal Adaptivity: GasNet Robot Control Networks (2002, background): “Recent interest in the Baldwin effect (Hinton & Nowlan, 1987; Mayley, 1996) has extended this picture, highlighting the role of lifetime plasticity in increasing the evolvability of the underlying solution class.”
Hybrid Genetic Algorithms: A Review (2006, background): “Baldwinian search can also have the effect of obscuring genetic differences and, thus, hindering the evolution process [105].”
Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills (2015, background): “Computational studies of evolving agents that learn have also shed light on open biological questions regarding the interactions between evolution and learning [50, 52, 53].”