Curbing Negative Influences Online for Seamless Transfer Evolutionary Optimization

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, particular...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2019-12, Vol.49 (12), p.4365-4378
Main Authors: Da, Bingshui, Gupta, Abhishek, Ong, Yew-Soon
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive algorithms
Analogies
Bayes methods
Computational modeling
Evolutionary algorithms
Machine learning
Multiple objective analysis
Negative transfer
online similarity learning
Optimization
Probabilistic logic
probabilistic models
Replicating
Search problems
Solvers
Task analysis
transfer evolutionary optimization
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Summary: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 single- and multi-objective optimization.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2018.2864345