Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Autonomous Robots
A Lamarckian Approach for Neural Network Training
Neural Processing Letters
Effect of synthetic emotions on agents’ learning speed and their survivability
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Biasing Coevolutionary Search for Optimal Multiagent Behaviors
IEEE Transactions on Evolutionary Computation
Multi-agent System Approach to React to Sudden Environmental Changes
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
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We model populations of classifiers which are aimed to function in permanently varying environments, adapt to unexpected changes, to comply fitness function and survive. A failure to fulfill survivability condition is resulting in unsuccessful agents being removed from the agent society and be replaced by newborns which inherit some upbringing learning information from parent agents. We split the agent population into groups and suggest storing agent's gains accumulated during most recent periods, distort randomly training signals and a level of survival threshold. A presence of optimal number of groups and a necessity of small groups with mutually collaborating agents is demonstrated.