Distributed evolution strategies for large-scale optimization

Duan，Qiqi1,2; Zhou，Guochen2; Shao，Chang3; Yang，Yijun3; Shi，Yuhui2

10.1145/3520304.3528784

2022-07-09

Genetic and Evolutionary Computation Conference (GECCO)

GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference

395-398

JUL 09-13, 2022

null,Boston,MA

1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES

ASSOC COMPUTING MACHINERY

As their underlying models are becoming larger and data-driven, an increasing number of modern real-world applications can be mathematically formulated as large-scale continuous optimization. In this paper, we propose a distributed evolution strategy (DES) for large-scale black-box optimization (specifically with memory-costly function evaluations), running on the mainstream clustering computing platform. In order to amortize the memory cost, DES utilizes the distributed shared memory to support parallelism of function evaluations. For better fitting into the scalable computing architecture of interest, DES adopts the well-known island model to distribute one low-rank version of covariance matrix adaptation (CMA), because the quadratic complexity of the standard CMA is not well scalable. For a proper trade-off between exploration and exploitation, DES needs to, on-the-fly, adjust strategy parameters at two time-scale levels. Experiments show its efficiency on most of test functions chosen.

black-box optimization evolution strategy island model

通讯

英语

EI ; CPCI-S

Shenzhen Fundamental Research Program[JCYJ20200109141235597]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:001035469400119

20223312576635

Covariance matrix ; Economic and social effects ; Evolutionary algorithms ; Memory architecture ; Optimization

Computer Systems and Equipment:722 ; Mathematics:921 ; Optimization Techniques:921.5 ; Numerical Methods:921.6 ; Social Sciences:971

2-s2.0-85136333627

Scopus

1.Harbin Institute of Technology,Harbin,China
2.Southern University of Science and Technology,Shenzhen,China
3.University of Technology Sydney,Sydney,Australia

Duan，Qiqi,Zhou，Guochen,Shao，Chang,et al. Distributed evolution strategies for large-scale optimization[C]. 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES:ASSOC COMPUTING MACHINERY,2022:395-398.