Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147937
Title: Autoencoding evolutionary search with learning across heterogeneous problems
Authors: Feng, Liang
Ong, Yew-Soon
Jiang, Siwei
Gupta, Abhishek
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Feng, L., Ong, Y., Jiang, S. & Gupta, A. (2017). Autoencoding evolutionary search with learning across heterogeneous problems. IEEE Transactions On Evolutionary Computation, 21(5), 760-772. https://dx.doi.org/10.1109/TEVC.2017.2682274
Journal: IEEE Transactions on Evolutionary Computation 
Abstract: To enhance the search performance of evolutionary algorithms, reusing knowledge captured from past optimization experiences along the search process has been proposed in the literature, and demonstrated much promise. In the literature, there are generally three types of approaches for reusing knowledge from past search experiences, namely exact storage and reuse of past solutions, the reuse of model-based information, and the reuse of structured knowledge captured from past optimized solutions. In this paper, we focus on the third type of knowledge reuse for enhancing evolutionary search. In contrast to existing works, here we focus on knowledge transfer across heterogeneous continuous optimization problems with diverse properties, such as problem dimension, number of objectives, etc., that cannot be handled by existing approaches. In particular, we propose a novel autoencoding evolutionary search paradigm with learning capability across heterogeneous problems. The essential ingredient for learning structured knowledge from search experience in our proposed paradigm is a single layer denoising autoencoder (DA), which is able to build the connections between problem domains by treating past optimized solutions as the corrupted version of the solutions for the newly encountered problem. Further, as the derived DA holds a closed-form solution, the corresponding reusing of knowledge from past search experiences will not bring much additional computational burden on the evolutionary search. To evaluate the proposed search paradigm, comprehensive empirical studies on the complex multiobjective optimization problems are presented, along with a real-world case study from the fiber-reinforced polymer composites manufacturing industry.
URI: https://hdl.handle.net/10356/147937
ISSN: 1089-778X
DOI: 10.1109/TEVC.2017.2682274
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TEVC.2017.2682274.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles
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