Gene Regulatory Network Evolution Through Augmenting Topologies
This paper presents a new algorithm for the evolution of artificial gene regulatory networks (GRNs), using a network distance metric for speciation and genetic similarity. The algorithm is inspired by the neuroevolution of augmenting topologies (NEAT) algorithm and demonstrates superior performance compared to standard GA and evolutionary programming across various experiments.
Jan 23, 2015