Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolving neural networks through augmenting topologies
Evolutionary Computation
A Taxonomy for artificial embryogeny
Artificial Life
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Generative encoding for multiagent learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Genetic team composition and level of selection in the evolution of cooperation
IEEE Transactions on Evolutionary Computation
Evolutionary robotics: the next-generation-platform for on-line and on-board artificial evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Coupled inverted pendulums: a benchmark for evolving decentral controllers in modular robotics
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A comparison between different encoding strategies for snake-like robot controllers
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Robotics and Autonomous Systems
Encouraging reactivity to create robust machines
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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In an application where autonomous robots can amalgamate spontaneously into arbitrary organisms, the individual robots cannot know a priori at which location in an organism they will end up. If the organism is to be controlled autonomously by the constituent robots, an evolutionary algorithm that evolves the controllers can only develop a single genome that will have to suffice for every individual robot. However, the robots should show different behaviour depending on their position in an organism, meaning their phenotype should be different depending on their location. In this paper, we demonstrate a solution for this problem using the HyperNEAT generative encoding technique with differentiated genome expression. We develop controllers for organism locomotion with obstacle avoidance as a proof of concept. Finally, we identify promising directions for further research.