Evolving Dynamical Neural Networks for Adaptive Behavior

By Randall D. Beer, John C. Gallagher. 1992

How researchers see this paper

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

Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Such networks were called continuous time recurrent neural networks (CTRNN).”
Evolutionary computation for modeling and optimization (2005, background): “The symbots are a radical simplification of a type of virtual robot investigated by Randall Beer and John Gallagher [12].”
Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot (1994, background): “Beside these solutions, some other researchers have ful lled the requirements of learning and adaptation by employing various sorts of neural networks to control a robotic system [2], [20]; whether pre-wired or plastic, these neural controllers exhibit characteristics of generalization, exibility, r”
Neuroevolution: from architectures to learning (2008, methodology): “Evolution of CTRNNs has been applied to difficult problems such as robot control [86].”
Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots (2001, background): “It may also be the case that networks of sigmoid neurons with synaptic time delays and continuous dynamics [3] could be evolved under the same conditions used for spiking neurons.”
Evolutionary Robotics and the Radical Envelope-of-Noise Hypothesis (1997, background): “Several experimenters including Jakobi, Husbands, and Harvey (1995), Beer and Gallagher (1992), Miglino, Lund, and Nol (1995) have shown that it is possible to evolve control architectures in simulation for a real robot.”
Evolutionary and Computational Advantages of Neuromodulated Plasticity (2008, methodology): “7 was used to describe the dynamics of network nodes in (Pearlmutter, 1990; Beer and Gallagher, 1992).”
Evolution of homing navigation in a real mobile robot (1996, methodology): “[8] to develop the control system of simulated robots [9], [10], [11], [12], [6], [13].”
Evolving artificial neural networks (1999, methodology): “For example, the evolutionary approach has been used to train recurrent ANN’s [41], [60], [65], [100], [102], [103], [106], [117], [126]–[128], higher order ANN’s [52], [53], and fuzzy ANN’s [76], [77], [129], [130].”
Evolving Artiicial Neural Networks (2007, methodology): “and multimodal spaces, which are the typical case in the real world, considerable research and application has been conducted on the evolution of connection weights [24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58”