Evolving Neural Networks Using the "Baldwin Effect"

By E. Boers, M. V. Borst, Ida G. Sprinkhuizen-Kuyper. 1995

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sebastian Risi
2. Kenneth O. Stanley
3. Andrea Soltoggio
4. Joel Lehman
5. Shimon Whiteson
6. Jean-Baptiste Mouret
7. Jeff Clune
8. Paul Tonelli

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.”
• Evolutionary Computation for Reinforcement Learning (2012, methodology): “This biologically intuitive combination, which has been applied to many computational systems (Hinton and Nowlan, 1987; Ackley and Littman, 1991; Boers et al, 1995; French and Messinger, 1994; Gruau and Whitley, 1993; Nolfi et al, 1994), can yield effective reinforcement-learning algorithms.”