Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Explorations in evolutionary robotics
Adaptive Behavior
Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Evolving mobile robots in simulated and real environments
Artificial Life
Reinforcement Learning
Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics
Proceedings of the Third European Conference on Advances in Artificial Life
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary behavior learning for action-based environment modeling by a mobile robot
Applied Soft Computing
Anticipatory Behavior in Adaptive Learning Systems
Intelligent neuro-controller for navigation of mobile robot
Proceedings of the International Conference on Advances in Computing, Communication and Control
Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia
Proceedings of the 12th annual conference on Genetic and evolutionary computation
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
A study of a soft computing based method for 3D scenario reconstruction
Applied Soft Computing
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Evolutionary Robotics (ER) is one of promising approaches to design robot controllers which essentially have complicated and/or complex properties. In most ER research, the sensory-motor mappings of robots are represented as artificial neural networks, and their connection weights (and sometimes the structure of the networks) can be optimized in the parameter spaces by using evolutionary computation. However, generally, the evolved neural controllers could be fragile in unexperienced environments, especially in real worlds, because the evolutionary optimization processes would be executed in idealized simulators. This is known as the gap problem between the simulated and real worlds. To overcome this, the author focused on evolving an on-line learning ability instead of weight parameters in a simulated environment. According to recent biological findings, actually, the kinds of on-line adaptation abilities can be found in real nervous systems of insects and crustaceans, and it is also known that a variety of neuromodulators (NMs) play crucial roles to regulate the network characteristics (i.e. activating/blocking/changing of synaptic connections). Based on this, a neuromodulatory neural network model was proposed and it was utilized as a mobile robot controller. In the paper, the detail behavior analysis of the evolved neuromodulatory neural network is also discussed.