Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139922
Title: Curbing negative influences online for seamless transfer evolutionary optimization
Authors: Da, Bingshui
Gupta, Abhishek
Ong, Yew-Soon
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Da, B., Gupta, A., & Ong, Y.-S. (2019). Curbing negative influences online for seamless transfer evolutionary optimization. IEEE Transactions on Cybernetics, 49(12), 4365-4378. doi:10.1109/TCYB.2018.2864345
Journal: IEEE Transactions on Cybernetics
Abstract: This paper draws motivation from the remarkable ability of humans to extract useful building-blocks of knowledge from past experiences and spontaneously reuse them for new and more challenging tasks. It is contended that successfully replicating such capabilities in computational solvers, particularly global black-box optimizers, can lead to significant performance enhancements over the current state-of-the-art. The main challenge to overcome is that in general black-box settings, no problem-specific data may be available prior to the onset of the search, thereby limiting the possibility of offline measurement of the synergy between problems. In light of the above, this paper introduces a novel evolutionary computation framework that enables online learning and exploitation of similarities across optimization problems, with the goal of achieving an algorithmic realization of the transfer optimization paradigm. One of the salient features of our proposal is that it accounts for latent similarities which while being less apparent on the surface, may be gradually revealed during the course of the evolutionary search. A theoretical analysis of our proposed framework is carried out, substantiating its positive influences on optimization performance. Furthermore, the practical efficacy of an instantiation of an adaptive transfer evolutionary algorithm is demonstrated on a series of numerical examples, spanning discrete, continuous, as well as singleand multi-objective optimization.
URI: https://hdl.handle.net/10356/139922
ISSN: 2168-2267
DOI: 10.1109/TCYB.2018.2864345
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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