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Intelligent Engineering Systems through Artificial Neural Networks, Volume 16

Editor
Cihan H. Dagli
Cihan H. Dagli
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Anna L. Buczak
Anna L. Buczak
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David L. Enke
David L. Enke
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Mark Embrechts
Mark Embrechts
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Okan Ersoy
Okan Ersoy
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ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006

As new species arise from biological evolution, new techniques arise when an evolutionary algorithm is used to train virtual robots. This study generalizes the ISAc list encoding for virtual robots working on the Tartarus task and tests a biologically inspired algorithmic technique called hybridization. A collection of 100 populations of virtual robots is trained in three distinct ways. A baseline experiment performs 1000 generations of evolution on each population. Two other collections of runs stop at intermediate points, harvest the currently best controllers from each population, and initialize new runs with the harvested controllers from all of the populations....

Abstract
1 Introduction
2 The Representation
3 Experimental Design
4 Results and Conclusions
Acknowledgments
References
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