Introduction to the theory of neural computation
Introduction to the theory of neural computation
Distributed artificial intelligence: theory and praxis
Distributed artificial intelligence: theory and praxis
Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Robust non-linear control through neuroevolution
Robust non-linear control through neuroevolution
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Evolving keepaway soccer players through task decomposition
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Evolution of collective behavior in a team of physically linked robots
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
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This research investigates an evolutionary approach to engineering agent collectives that accomplish tasks cooperatively. In general, reproduction and selection form the two cornerstones of evolution and in this paper we study various reproduction schemes in an evolving population of agents. We classify reproduction schemes in temporal and spatial terms, that is, by distinguishing when and where agents reproduce. In terms of the temporal dimension, we tested schemes where agents reproduce only at the end of their lifetime or multiple times during their lifetime. In terms of the spatial dimension we distinguished locally restricted reproduction (agents reproduce only with agents in adjacent positions) and panmictic reproduction (when an agent can reproduce with any other in the environment). This classification leads to four different reproduction schemes, which we compare, via their overall impact upon collective performance. Results using two completely different types of evolvable controllers (hand-coded or neural-net based) indicate that utilizing single reproduction at the end of an agent’s lifetime and locally restricted reproduction afforded the agent collective a significantly higher level of performance in its cooperative task.