A sensitivity analysis of contribution-based cooperative co-evolutionary algorithms

Kazimipour, B, Li, X, Omidvar, M and Qin, K 2015, 'A sensitivity analysis of contribution-based cooperative co-evolutionary algorithms', in Proceedings of Congress of Evolutionary Computation (CEC 2015), Sendai, Japan, 25 - 28 May 2015, pp. 417-422.


Document type: Conference Paper
Collection: Conference Papers

Title A sensitivity analysis of contribution-based cooperative co-evolutionary algorithms
Author(s) Kazimipour, B
Li, X
Omidvar, M
Qin, K
Year 2015
Conference name CEC 2015
Conference location Sendai, Japan
Conference dates 25 - 28 May 2015
Proceedings title Proceedings of Congress of Evolutionary Computation (CEC 2015)
Publisher IEEE
Place of publication United States
Start page 417
End page 422
Total pages 6
Abstract Cooperative Co-evolutionary (CC) techniques have demonstrated the promising performance in dealing with large-scale optimization problems. However, in many applications, their performance may drop due to the presence of imbalanced contributions to the objective function value from different subsets of decision variables. To remedy this drawback, Contribution-Based Cooperative Co-evolutionary (CBCC) algorithms have been proposed. They have presented significant improvements over traditional CC techniques when the decomposition is accurate and the imbalance level is very high. However, in real-world scenarios, we might not have the knowledge about the ideal decomposition and actual imbalance level of a problem to be solved. Therefore, this study aims at analysing the performance of existing CBCC techniques in more realistic settings, i.e., when the decomposition error is unavoidable and the imbalance level is low or moderate. Our in-depth analysis reveals that even in these situations, CBCC algorithms are superior alternatives to traditional CC techniques. We also observe that the variations of CBCC techniques may lead to the significantly different performance. Thus, we recommend practitioners to carefully choose a competent variant of CBCC which best suits their particular applications.
Subjects Neural, Evolutionary and Fuzzy Computation
DOI - identifier 10.1109/CEC.2015.7256920
Copyright notice © 2015 IEEE
ISBN 9781479974924
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