This paper introduces a model of evolving genetically-regulated swarms within an eco-evolutionary system, highlighting the benefits of competitive selection pressure for collision avoidance, which leads to improved navigation abilities and overall agent behavior.
Sep 4, 2017
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
This study uses neuromodulatory gene regulatory networks to improve reinforcement learning agents in solving the robot coverage control problem, demonstrating better generalization and learning capabilities compared to fixed parameter settings.
Aug 4, 2013