An Autoencoder-embedded Evolutionary Optimization Framework for High-dimensional Problems

Meiji Cui, Li Li, Meng Chu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many ever-increasingly complex engineering optimization problems fall into the class of High-dimensional Expensive Problems (HEPs), where fitness evaluations are very time-consuming. It is extremely challenging and difficult to produce promising solutions in high-dimensional search space. In this paper, an Autoencoder-embedded Evolutionary Optimization (AEO) framework is proposed for the first time. As an efficient dimension reduction tool, an autoencoder is used to compress high-dimensional landscape to informative low-dimensional space. The search operation in this low-dimensional space can facilitate the population converge towards the optima more efficiently. To balance the exploration and exploitation ability during optimization, two sub-populations coevolve in a distributed fashion, where one is assisted by an autoencoder and the other undergoes a regular evolutionary process. The information between these two sub-populations are dynamically exchanged. The proposed algorithm is validated by testing several 200 dimensional benchmark functions. Compared with the state-of-art algorithms for HEPs, AEO shows extraordinarily high efficiency for these challenging problems.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1046-1051
Number of pages6
Volume2020-October
ISBN (Electronic)9781728185262
DOIs
StatePublished - Oct 11 2020
Event2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada
Duration: Oct 11 2020Oct 14 2020

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
CountryCanada
CityToronto
Period10/11/2010/14/20

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • High-dimensional expensive problems (HEPs)
  • artificial intelligence
  • autoencoder
  • dimension reduction
  • evolutionary algorithms
  • machine learning.

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