Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Explorations in evolutionary robotics
Adaptive Behavior
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Autonomous Robots
Autonomous Robots
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Evolving Robot Behaviours with Diffusing Gas Networks
Proceedings of the First European Workshop on Evolutionary Robotics
Evolving Neural Networks through Augmenting Topologies
Evolving Neural Networks through Augmenting Topologies
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolving Self-Organizing Behaviors for a Swarm-Bot
Autonomous Robots
Multimode locomotion via SuperBot reconfigurable robots
Autonomous Robots
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Evolving modular genetic regulatory networks
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolutionary Swarm Robotics: Evolving Self-Organising Behaviours in Groups of Autonomous Robots (Studies in Computational Intelligence) (Studies in Computational Intelligence)
Fitness functions in evolutionary robotics: A survey and analysis
Robotics and Autonomous Systems
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Symbricator3D --- A Distributed Simulation Environment for Modular Robots
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Scalable self-assembly and self-repair in a collective of robots
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Coupled inverted pendulums: a benchmark for evolving decentral controllers in modular robotics
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolving a novel bio-inspired controller in reconfigurable robots
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part I
Artificial homeostatic system: a novel approach
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Autonomous Self-Reconfiguration of Modular Robots by Evolving a Hierarchical Mechanochemical Model
IEEE Computational Intelligence Magazine
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Self-learning fuzzy controllers based on temporal backpropagation
IEEE Transactions on Neural Networks
Coupled inverted pendulums: a benchmark for evolving decentral controllers in modular robotics
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Impact of neuron models and network structure on evolving modular robot neural network controllers
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Virtual spatiality in agent controllers: encoding compartmentalization
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
The Endocrine Control Evolutionary Algorithm: an extensible technique for optimization
Natural Computing: an international journal
Hi-index | 0.00 |
One of the main challenges in automatic controller synthesis is to develop methods that can successfully be applied for complex tasks. The difficulty is increased even more in the case of settings with multiple interacting agents. We apply the artificial homeostatic hormone system (AHHS) approach, which is inspired by the signaling network of unicellular organisms, to control a system of several independently acting agents decentrally. The approach is designed for evaluation-minimal, artificial evolution in order to be applicable to complex modular robotics scenarios. The performance of AHHS controllers is compared with neuroevolution of augmenting topologies (NEAT) in the coupled inverted pendulums benchmark. AHHS controllers are found to be better for multimodular settings. We analyze the evolved controllers with regard to the usage of sensory inputs and the emerging oscillations, and we give a nonlinear dynamics interpretation. The generalization of evolved controllers to initial conditions far from the original conditions is investigated and found to be good. Similarly, the performance of controllers scales well even with module numbers different from the original domain the controller was evolved for. Two reference implementations of a similar controller approach are reported and shown to have shortcomings. We discuss the related work and conclude by summarizing the main contributions of our work.