Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Levels of dynamics and adaptive behavior in evolutionary neural controllers
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolution of Adaptive Synapses: Robots with Fast Adaptive Behavior in New Environments
Evolutionary Computation
Journal of Cognitive Neuroscience
Adding learning to the cellular development of neural networks: Evolution and the baldwin effect
Evolutionary Computation
How novelty search escapes the deceptive trap of learning to learn
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Novelty of behaviour as a basis for the neuro-evolution of operant reward learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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
Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Indirectly encoding neural plasticity as a pattern of local rules
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Analog Genetic Encoding for the Evolution of Circuits and Networks
IEEE Transactions on Evolutionary Computation
On the Performance of Indirect Encoding Across the Continuum of Regularity
IEEE Transactions on Evolutionary Computation
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
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The present paper analyzes the mutual relationships between generative and developmental systems (GDS) and synaptic plasticity when evolving plastic artificial neural networks (ANNs) in reward-based scenarios. We first introduce the concept of synaptic Transitive Learning Abilities (sTLA), which reflects how well an evolved plastic ANN can cope with learning scenarios not encountered during the evolution process. We subsequently report results of a set of experiments designed to check that (1) synaptic plasticity can help a GDS to fine-tune synaptic weights and (2) that with the investigated generative encoding (EvoNeuro), only a few learning scenarios are necessary to evolve a general learning system, which can adapt itself to reward-based scenarios not tested during the fitness evaluation.