NDLI: An efficient differential evolution using speeded-up k-nearest neighbor estimator

Content Provider	Springer Nature Link
Author	Park, So Youn Lee, Ju Jang
Copyright Year	2013
Abstract	In evolutionary algorithm, one of the main issues is how to reduce the number of fitness evaluations required to obtain optimal solutions. Generally a large number of evaluations are needed to find optimal solutions, which leads to an increase of computational time. Expensive cost may have to be paid for fitness evaluation as well. Differential evolution (DE), which is widely used in many applications due to its simplicity and good performance, also cannot escape from this problem. In order to solve this problem a fitness approximation model has been proposed so far, replacing real fitness function for evaluation. In fitness approximation, an ability to estimate accurate value with compact structure is needed for good performance. Therefore in this paper we propose an efficient differential evolution using fitness estimator. We choose k-nearest neighbor (kNN) as fitness estimator because it does not need any training period or complex computation. However too many training samples in the estimator may cause computational complexity to be exponentially high. Accordingly, two schemes with regard to accuracy and efficiency are proposed to improve the estimator. Our proposed algorithm is tested with various benchmark functions and shown to find good optimal solutions with less fitness evaluation and more compact size, compared with DE and DE-kNN.
Starting Page	35
Ending Page	49
Page Count	15
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	18
Issue Number	1
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-05-16
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Differential evolution k-Nearest neighbor Regression Fitness approximation Meta-model Surrogate model Memetic computing Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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