Title:	A NOISE-TOLERANT EVOLUTIONARY ALGORITHM BASED ON ACCUMULATIVE SAMPLING
Author(s):	Jeongmin Kim, Steven Kurniawan and Kwang Ryel Ryu
ISBN:	978-989-8533-92-0
Editors:	Ajith P. Abraham and Jörg Roth
Year:	2019
Edition:	Single
Keywords:	Noisy Optimization, Evolutionary Algorithm, Restricted Tournament Selection
Type:	Poster
First Page:	245
Last Page:	247
Language:	English
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Paper Abstract:	This paper proposes an evolutionary algorithm that can efficiently solve noisy optimization problems for which the objective functions can be evaluated not accurately but only approximately. While the accuracy of evaluation may be improved by taking many samples of evaluation and taking the average, it can be computationally demanding. The proposed algorithm is an improvement over a previous work to achieve higher search efficiency by better allotting different number of samples to different candidate solutions in the population during the course of evolution. Experiments with a set of benchmark problems show that the proposed algorithm significantly outperforms the previous algorithm.
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