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How to Select a Winner in Evolutionary Optimization? (2017)
Risto Miikkulainen
,
Hormoz Shahrzad
, Nigel Duffy, and Phil Long
In many evolutionary optimization domains evaluations are noisy. The candidates are tested on a number of randomly drawn samples, such as different games played, different physical simulations, or different user interactions. As a result, selecting the winner is a multiple hypothesis problem: The candidate that evaluated the best most likely received a lucky selection of samples, and will not perform as well in the future. This paper proposes a technique for selecting the winner and estimating its true performance based on the smoothness assumption: Candidates that are similar perform similarly. Estimated fitness is replaced by the average fitness of candidate's neighbors, making the selection and estimation more reliable. Simulated experiments in the multiplexer domain show that this technique is reliable, making it likely that the true winner is selected and its future performance is accurately estimated.
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Citation:
In
Proceedings of the IEEE Symposium Series in Computational Intelligence
2017. IEEE.
Bibtex:
@inproceedings{miikkulainen:ssci17, title={How to Select a Winner in Evolutionary Optimization?}, author={Risto Miikkulainen and Hormoz Shahrzad and Nigel Duffy and Phil Long}, booktitle={Proceedings of the IEEE Symposium Series in Computational Intelligence}, publisher={IEEE}, url="http://www.cs.utexas.edu/users/ai-lab?miikkulainen:ssci17", year={2017} }
People
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Hormoz Shahrzad
Masters Alumni
hormoz [at] cognizant com
Areas of Interest
Applications
Evolutionary Computation
Labs
Neural Networks