Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Integrating reactive, sequential, and learning behavior using dynamical neural networks
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolving action selection and selective attention without actions, attention, or selection
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms
The 3rd International Symposium on Experimental Robotics III
Coevolution and the Red Queen effect shape virtual plants
Genetic Programming and Evolvable Machines
Integration of evolution with a robot action selection model
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
An effective robotic model of action selection
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
Evolution of homing navigation in a real mobile robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The evolution of an effective central model of action selection and behavioral modules have already been revised in previous papers. The central model has been set to resolve a foraging task, where specific modules for exploring the environment and for handling the collection and delivery of cylinders have been developed. Evolution has been used to adjust the selection parameters of the model and the neural weights of the exploring behaviors. However, in this paper the focus is on the use of genetic algorithms for coevolving both the selection parameters and the exploring behaviors. The main goal of this study is to reduce the number of decisions made by the human designer.