Issues in evolutionary robotics
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Evolving neural networks through augmenting topologies
Evolutionary Computation
Evolving mobile robots able to display collective behaviors
Artificial Life
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
How novelty search escapes the deceptive trap of learning to learn
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Revising the evolutionary computation abstraction: minimal criteria novelty search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Evolving a diversity of virtual creatures through novelty search and local competition
Proceedings of the 13th annual conference on Genetic and evolutionary computation
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Simbad: an autonomous robot simulation package for education and research
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Generic behaviour similarity measures for evolutionary swarm robotics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Novelty search is a recent and promising evolutionary technique. The main idea behind it is to reward novel solutions instead of progress towards a fixed goal, in order to avoid premature convergence and deception. In this paper, we use novelty search together with NEAT, to evolve neuro-controllers for a swarm of simulated robots that should perform an aggregation task. In the past, novelty search has been applied to single robot systems. We demonstrate that novelty search can be applied successfully to multirobot systems, and we discuss the challenges introduced when moving from a single robot setup to a multirobot setup. Our results show that novelty search can outperform the fitness-based evolution in swarm robotic systems, finding (i) a more diverse set of successful solutions to an aggregation task, (ii) solutions with higher fitness scores earlier in the evolutionary runs, and (iii) simpler solutions in terms of the topological complexity of the evolved neural networks.