Design Strategies for Evolutionary Robotics

@inproceedings{Murray1995DesignSF,
  title={Design Strategies for Evolutionary Robotics},
  author={Andrew Murray and Sushil J. Louis},
  year={1995}
}
This paper deals with the question of how to balance evolutionary design and human expertise in order to best design robots which can learn speciic tasks. We study two behavioral tasks, approach and avoidance, and provide some preliminary results. 
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This paper studies how to balance evolutionary design and human expertise in order to best design situated autonomous agents which can learn speciic tasks. A genetic algorithm designs controlExpand
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i Acknowledgments I would rst like to express my appreciation to Dr. Sushil J. Louis, my thesis advisor, for his guidance and support throughout this research. It was he that initiated and nurturedExpand
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References

SHOWING 1-9 OF 9 REFERENCES
Adapting Control Strategies for Situated Autonomous
This paper studies how to balance evolutionary design and human expertise in order to best design situated autonomous agents which can learn speciic tasks. A genetic algorithm designs controlExpand
Designer Genetic Algorithms: Genetic Algorithms in Structure Design
TLDR
A genetic algorithm is described that uses di erential information about search direction to design structures that is captured by a masked crossover operator which also removes the bias toward short schemas. Expand
Evolving visually guided robots
TLDR
Results are presented which demonstrate that neural network control architectures can be evolved for an accurate simulation model of a visually guided robot. Expand
Genetic Algorithms in Search Optimization and Machine Learning
TLDR
This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Expand
Adaptation in natural and artificial systems
TLDR
Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI. Expand
Robot shaping: developing situated agents through learning
Modular row housing used to construct row housing units of one story or more comprising a plurality of modular units, each having a height of one story and a width corresponding to one-half of theExpand
\Design strategies for evolutionary robotics
  • Proceedings of the Third Golden West International Conference on Intelligent Systems
  • 1995
Husbands, and I. Harvey. \Evolving visually guided robots
  • Husbands, and I. Harvey. \Evolving visually guided robots
  • 1992
Adaptation In Natural and Arti cial Systems. The University of Michigan Press
  • Ann Arbour,
  • 1975