Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Explorations in evolutionary robotics
Adaptive Behavior
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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
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
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Choosing one option from a sequence of possibilities seen one at a time is a common problem facing agents whenever resources, such as mates or habitats, are distributed in time or space. Optimal algorithms have been developed for solving a form of this sequential search task known as the Dowry Problem (finding the highest dowry in a sequence of 100 values); here we explore whether continuous time recurrent neural networks (CTRNNs) can be evolved to perform adaptively in Dowry Problem scenarios, as an example of minimally cognitive behavior [Beer, 1996]. We show that even 4-neuron CTRNNs can successfully solve this sequential search problem, and we offer some initial analysis of how they can achieve this feat.