RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support


Short Retraction Notice
The paper does not meet the standards of "Open Journal of Optimization".
This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's Retraction Guidelines. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.
Editor guiding this retraction: Prof. Moran Wang (EiC of TEL)
The full retraction notice in PDF is preceding the original paper, which is marked "RETRACTED".

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[1] Kumar, S. (2016) Selection of Optimal Path in Routing Using Genetic Algorithm. International Journal of Latest Trends in Engineering and Technology, 7, 259-263.
[2] Jensen, M.T. (2003) Generating Robust and Flexible Job Shop Schedules Using Genetic Algorithms. IEEE Transactions on Evolutionary Computation, 7, 275-288.
[3] Alobaidi, W., Sandgren, E. and Alkuam, E. (2017) Decision Support through Intelligent Agent Based Simulation and Multiple Goal Based Evolutionary Optimization. Intelligent Information Management, 9, 97-113.
[4] Sun, KT., Lin, Y.C., Wu, C.Y. and Huang, Y.M. (2009) An Application of the Genetic Programming Technique to Strategy Development. Expert Systems with Applications, 36, 5157-5161.
[5] Compare, M. and Martini, F. (2015) Decision Support: Genetic Algorithms for Condition Based Maintenance Optimization under Uncertainty. European Journal of Operations Research, 16, 611-623.
[6] Guido, R. and Conforti, D. (2015) A Hybrid Genetic Approach for Solving an Integrated Multi-Objective Operating Room Planning and Scheduling Problem. Computers and Operations Research.
[7] OnkHam, W., Karwowski, W. and Ahram, T.Z. (2012) Economics of Human Performance and Systems Total Ownership Cost. Work, 41, 2781-2788.
[8] Liang, P.L. and Jones, C. (1987) Design of a Self-Evolving Decision Support System. Journal of Management Information Systems, 4, 59-82.
[9] Lin, S. and Kernighan, B.W. (1973) An Efficient Heuristic Algorithm for the Traveling Salesman Problem. Operations Research, 21, 498-516.
[10] Nonas, E. and Poulovassilis, A. (1999) Optimising Self Adaptive Networks by Evolving Rule-Based Agents. Lecture Notes in Computer Science, 1596, 203-214.
[11] Ishibuchi, H. and Yamamoto, T. (2004) Fuzzy Rule Selection by Multo-Objective Genetic Local Search Algorithms and Rule Evaluation Measures in Data Mining. Fuzzy Sets and Systems, 141, 59-88.
[12] Hadian, A., Nasiri, M. and Minaci-Bidgoli, B. (2010) Clustering Based Multi-Objective Rule Mining Using Genetic Algorithm. International Journal of Digital Content Technology and Its Application, 4, 37-42.
[13] Caponetto, R., Fortuna, S.F. and Xibilia, M.G. (2003) Chaotic Sequences to Improve the Performance of Evolutionary Algorithms. IEEE Transactions on Evolutionary Computing, 7, 289-304.
[14] Mozaffari, A., Emami, M., Azad, N.L. and Fathi, A. (2015) On the Effcacy of Chaos-Enhanced Heuristic Walks with Nature-Based Controllers for Robust and Accurate Intellegent Search. Journal of Experimental & Theoretical Artificial Intelligence, 27, 389-422.
[15] Srinivasan, S. and Ramakrishnan, S. (2013) A Social Intellegent System for Multi-objective Optimization of Classification Rules Using Cultural Algorithms. Computing, 95, 327-350.
[16] Webb, D. and Sandgren, E. (2017) Topological Design via a Rule Based Genetic Optimization Algorithm. Computers and Structures, Submitted.
[17] Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA.
[18] Davis, L. (1991) Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York.
[19] Imam, A.A, Much, A.M. and Alamsyah, A. (2016) Comparison of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimization. Scientific Journal of Informatics, 3, 51-60.
[20] Wang, L., Cai, J., Li, M. and Liu, Z. (2017) Flexible Shop Scheduling Problem Using an Improved Ant Colony Optimization. Scientific Programming, 2017, Article ID: 9016303.
[21] Schyns, M. (2015) Discrete Optimization: An Ant Colony System for Responsive Dynamic Vehicle Routing. European Journal of Operational Research, 245, 704-718.
[22] Sandgren, E. and Ragsdell, K.M. (1980) The Utility of Nonlinear Programming Algorithms: A Comparative Study—Part I. Journal of Mechanical Design, Transactions of the ASME, 102, 540-546.

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