Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
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
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving policy geometry for scalable multiagent learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Constraining connectivity to encourage modularity in HyperNEAT
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
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Natural brains effectively integrate multiple sensory modalities and act upon the world through multiple effector types. As researchers strive to evolve more sophisticated neural controllers, confronting the challenge of multimodality is becoming increasingly important. As a solution, this paper presents a principled new approach to exploiting indirect encoding to incorporate multimodality based on the HyperNEAT generative neuroevolution algorithm called the multi-spatial substrate (MSS). The main idea is to place each input and output modality on its own independent plane. That way, the spatial separation of such groupings provides HyperNEAT an a priori hint on which neurons are associated with which that can be exploited from the start of evolution. To validate this approach, the MSS is compared with more conventional approaches to HyperNEAT substrate design in a multiagent domain featuring three input and two output modalities. The new approach both significantly outperforms conventional approaches and reduces the creative burden on the user to design the layout of the substrate, thereby opening formerly prohibitive multimodal problems to neuroevolution.