Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Evolutionary Swarm Robotics: Evolving Self-Organising Behaviours in Groups of Autonomous Robots (Studies in Computational Intelligence) (Studies in Computational Intelligence)
Online and onboard evolution of robotic behavior using finite state machines
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
An on-line on-board distributed algorithm for evolutionary robotics
EA'11 Proceedings of the 10th international conference on Artificial Evolution
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This paper is concerned with \textit{on-line} evolutionary robotics, where robot controllers are being evolved during a robots' operative time. This approach offers the ability to cope with environmental changes without human intervention, but to be effective it needs an automatic parameter control mechanism to adjust the evolutionary algorithm (EA) appropriately. In particular, mutation step sizes ($\sigma$) and the time spent on fitness evaluation ($\tau$) have a strong influence on the performance of an EA. In this paper, we introduce and experimentally validate a novel method for self-adapting $\tau$ during runtime. The results show that this mechanism is viable: the EA using this self-adaptative control scheme consistently shows decent performance without a priori tuning or human intervention during a run.