Please use this identifier to cite or link to this item: https://doi.org/10.1080/00207721.2011.618645
Title: A data mining approach to evolutionary optimisation of noisy multi-objective problems
Authors: Chia, J.Y.
Goh, C.K.
Shim, V.A.
Tan, K.C. 
Keywords: data mining
evolutionary computations
multi-objective optimisation
noise
Issue Date: 1-Jul-2012
Source: Chia, J.Y., Goh, C.K., Shim, V.A., Tan, K.C. (2012-07-01). A data mining approach to evolutionary optimisation of noisy multi-objective problems. International Journal of Systems Science 43 (7) : 1217-1247. ScholarBank@NUS Repository. https://doi.org/10.1080/00207721.2011.618645
Abstract: Many real world optimisation problems have opposing objective functions which are subjected to the influence of noise. Noise in the objective functions can adversely affect the stability, performance and convergence of evolutionary optimisers. This article proposes a Bayesian frequent data mining (DM) approach to identify optimal regions to guide the population amidst the presence of noise. The aggregated information provided by all the solutions helped to average out the effects of noise. This article proposes a DM crossover operator to make use of the rules mined. After implementation of this operator, a better convergence to the true Pareto front is achieved at the expense of the diversity of the solution. Consequently, an ExtremalExploration operator will be proposed in the later part of this article to help curb the loss in diversity caused by the DM operator. The result is a more directive search with a faster convergence rate. The search is effective in decision space where the Pareto set is in a tight cluster. A further investigation of the performance of the proposed algorithm in noisy and noiseless environment will also be studied with respect to non-convexity, discontinuity, multi-modality and uniformity. The proposed algorithm is evaluated on ZDT and other benchmarks problems. The results of the simulations indicate that the proposed method is effective in handling noise and is competitive against the other noise tolerant algorithms. © 2012 Taylor & Francis.
Source Title: International Journal of Systems Science
URI: http://scholarbank.nus.edu.sg/handle/10635/54050
ISSN: 00207721
DOI: 10.1080/00207721.2011.618645
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

10
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

8
checked on Nov 17, 2017

Page view(s)

40
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.