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Memristor Parallel Computing for a Matrix-Friendly Genetic Algorithm

Matrix operation is easy to be paralleled by hardware, and the memristor network can realize a parallel matrix computing model with in-memory computing. This article proposes a matrix-friendly genetic algorithm (MGA), in which the population is represented by a matrix and the evolution of population is realized by matrix operations. Compared with the performance of a baseline genetic algorithm (GA) on solving the maximum value of the binary function, MGA can converge better and faster. In addition, MGA is more efficient because of its parallelism on matrix operations, and MGA runs 2.5 times faster than the baseline GA when using the NumPy library. Considering the advantages of the memristor in matrix operations, memristor circuits are designed for the deployment of MGA. This deployment method realizes the parallelization and in-memory computing (memristor is both memory and computing unit) of MGA. In order to verify the effectiveness of this deployment, a feature selection experiment of logistic regression (LR) on Sonar datasets is completed. LR with MGA-based feature selection uses 46 fewer features and achieves 11.9% higher accuracy.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.26 No.5 October 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 901-910

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

FEATURE SELECTION
PARALLEL COMPUTING
GENETIC ALGORITHMS (GAS)
MEMRISTORS

Info Detail Spesifik

DOI: 10.1109/TEVC.2022.3144419

Pernyataan Tanggungjawab

Yongbin Yu, Jiehong Mo, Quanxin Deng, Chen Zhou, Biao Li, Xiangxiang Wang, Nijing Yang, Qian Tang, Xiao Feng,

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