Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Eeect

By Darrell WhitleyD. 1993

How researchers see this paper

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

1. Risto Miikkulainen
2. Jason Zhi Liang
3. Andrés Camero
4. Kenneth O. Stanley
5. J. Urgen Schmidhuber
6. Aditya Rawal
7. Elliot Meyerson
8. Faustino J. Gomez

Citations in the corpus

• Evolving Deep Neural Networks (2017, methodology): “Further, techniques such as Cellular Encoding [18] and NEAT [43] have been developed to evolve the topology of the neural network, which is particularly effective in determining the required recurrence.”
• Exploring the effects of Lamarckian and Baldwinian learning in evolving recurrent neural networks (1997, unknown): “In this paper, we explore the e ects of two approaches of embedding learning into GAs, namely the Lamarckian and the Baldwinian1 learning mechanisms [1], [3], [4], [7].”
• Symbiotic Evolution of Neural Networks in Sequential Decision Tasks (1997, background): “Several researchers have investigated the use of local learning after each action in an evolutionary algorithm (Grefenstette 1991; Gruau and Whitley 1993; Littman 1995; Nol and Parisi 1995).”