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 language | English (US) |
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Title of host publication | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1046-1051 |
Number of pages | 6 |
Volume | 2020-October |
ISBN (Electronic) | 9781728185262 |
DOIs | |
State | Published - Oct 11 2020 |
Event | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada Duration: Oct 11 2020 → Oct 14 2020 |
Conference
Conference | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 |
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Country/Territory | Canada |
City | Toronto |
Period | 10/11/20 → 10/14/20 |
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.