Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
The role of constraints in Hebbian learning
Neural Computation
On the dynamics of small continuous-time recurrent neural networks
Adaptive Behavior - Special issue on computational neuroethology
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Some Problems (and a Few Solutions) for Open-Ended Evolutionary Robotics
Proceedings of the First European Workshop on Evolutionary Robotics
Evolution of Adaptive Synapses: Robots with Fast Adaptive Behavior in New Environments
Evolutionary Computation
Parameter space structure of continuous-time recurrent neural networks
Neural Computation
Toward Spinozist Robotics: Exploring the Minimal Dynamics of Behavioral Preference
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Extended Homeostatic Adaptation: Improving the Link between Internal and Behavioural Stability
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Behavioural plasticity in autonomous agents: a comparison between two types of controller
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
New models for old questions: evolutionary robotics and the 'A not B' error
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Evolving plastic neural networks for online learning: review and future directions
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Self-regulating neurons in the sensorimotor loop
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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In evolutionary robotics, plastic neural network models proved to be promising for evolving adaptive behaviors. In particular, neurocontrollers incorporating hebbian synapses have been shown to be useful for implementing conflicting sub-behaviors. Numerous interesting complex tasks assume such flexibility. However, those evolved controllers often exhibit behavioral instability, as simulation time is extended beyond the short limit used during evolution. In this paper, we propose constrained plastic models inspired by neural homeostasis phenomena, in order to evolve flexible and stable pattern generators for single-legged locomotion. Comparative results show that constrained controllers perform better than unconstrained ones in both terms of evolvability and behavioral stability. Functional analyses of the best evolved controller unveil the adaptivity, robustness and homeostasis arising from the statically constrained plasticity. Interestingly, homeostasis evolved implicitly without relying on any active homeostatic mechanisms and is implemented through hebbian plasticity, usually considered destabilizing.