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Memetic Computing
Erschienen in: Memetic Computing 3/2021

28.07.2021 | Regular research paper

Linear prediction evolution algorithm: a simplest evolutionary optimizer

verfasst von: Cong Gao, Zhongbo Hu, Wangyu Tong

Erschienen in: Memetic Computing | Ausgabe 3/2021

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Abstract

The prediction-based evolutionary algorithms are a recently developed branch of metaheuristic algorithms. The most notable feature of this kind of algorithms is the use of a certain prediction model to develop their reproduction operators for evolution. The linear least square fitting model, as a simplest and most widely used statistic model, is first introduced to construct a linear prediction evolution algorithm (LPE) in this paper. Firstly, the proposed LPE randomly selects three individuals from three consecutive populations, respectively, and then fits a line on each dimension of the three individuals by using the linear least square fitting model. Finally, LPE regards the line expression as its reproduction operator to generate the offspring individuals. LPE algorithm does not have any control parameters except for a population size. Its reproduction operator based on the linear least square fitting model holds solid mathematical foundation without any empirical coefficients, and is theoretically proven to be adaptive to the variation of population regions. The effectiveness of the proposed LPE is validated on CEC2014, CEC2017 benchmark functions and a comprehensive set of seven engineering design problems. The comparison experiments indicate that LPE is a competitive optimizer compared with other state-of-the-art algorithms.
Vorheriger Artikel A study on multiform multi-objective evolutionary optimization
Nächster Artikel An adaptive niching method based on multi-strategy fusion for multimodal optimization

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Metadaten
Titel
Linear prediction evolution algorithm: a simplest evolutionary optimizer
verfasst von
Cong Gao
Zhongbo Hu
Wangyu Tong
Publikationsdatum
28.07.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 3/2021
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-021-00340-x

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