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DOI: http://dx.doi.org/10.7551/978-0-262-32621-6-ch074
Pages 455-456
First published 30 July 2014

Abstract of: "On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks"

Jean-Baptiste Mouret and Paul Tonelli

Abstract (Excerpt)

A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected.