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4. A New Index for Evolutionary Algorithms to Measure the Noise Level in Noisy Environments

Author

Junhua Li, Dongke Hu, and Hao Chen

Subjects
education.field_of_study, Index (economics), Degree (graph theory), Computer science, business.industry, Population, Evolutionary algorithm, 02 engineering and technology, Machine learning, computer.software_genre, Measure (mathematics), Resampling, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Noise level, business, education, computer
Abstract

In noisy situation, multiobjective evolutionary algorithm will lose the ability to search optimum results. The reason of this phenomenon is evolutionary algorithma shows the disorder of domination relationship to population individual. Focusing to this problem, lots of research have been made. One of them is the method resampling. The method is indeed improving the bad domination relationship of population individual in the noisy situation. However, there is no clear data parameter to show the degree of improving domination relationship when using the resampling method. So this paper presents a new index as a proof that with the increase of the number of resampling times, the proportion of correct judgment in the noisy situation of multiobjective evolutionary algorithm's domination relationship is also increasing.

Published
2018

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