A Novel Mechanism for Efficient the Search Optimization of Genetic Algorithm

References

• E. Alfaro-Cid, E. W. McGookin, and D. J. Murray-Smith, A comparative study of genetic operators for controller parameter optimization, Control Eng. Pract. 17 (2009) 185–197. doi: 10.1016/j.conengprac.2008.06.001
   Web of Science ®Google Scholar
• J. H. Cheng, S. W. Chen, and F. Y. Chen, Exploring how inter-organizational relational benefits affect information sharing in supply chains, Inf. Tech. Manage. 14 (2013) 283–294. doi: 10.1007/s10799-013-0165-x
   Web of Science ®Google Scholar
• K. DeJong, Parameter setting in EAs: a 30 year perspective, Parameter Setting in Evol. Algorithm. 54 (2007) 1–18. doi: 10.1007/978-3-540-69432-8_1
   Google Scholar
• K. Deep, and P. K. Singh, Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm, J. Comput. Chem. 35 (2015) 155–163.
   Google Scholar
• A. E. Eiben, Z. Michalewicz, M. Schoenauer, and J.E. Smith, Parameter control in evolutionary algorithms, Parameter Setting in Evol. Algorithm. 54 (2007) 19–46. doi: 10.1007/978-3-540-69432-8_2
   Google Scholar
• L. J. Eshelman, R. A. Caruana, and J. D. Schaer, Biases in the crossover landscape. ICGA, George Mason University, 1989.
   Google Scholar
• H. Fazlollahtabar, R. Hassanzadeh, I. Mahdavi, and N. Mahdavi-Amiri, A genetic optimization algorithm and perceptron learning rules for a bi-criteria parallel machine scheduling, J. Chi. Inst. Ind. Eng. 29 (2012) 206–218.
   Google Scholar
• T. C. Fogarty, Varying the probability of mutation in the genetic algorithm, Proc. 3rd Int. Conf. Gene. Algorithm. 1989, 104–109.
   Google Scholar
• M. S. Gibbs, G. C. Dandy and H. R. Maier, A genetic algorithm calibration method based on convergence due to genetic drift, Inf. Sci. 178 (2008) 2857–2869.
   Google Scholar
• T. D. Giddens, The determination of parameters and operators of the traditional genetic algorithm using an adaptive genetic algorithm generator. PhD Thesis, Texas Tech University. 1994.
   Google Scholar
• K. Grolinger, MAM. Capretz, A. Cunha, and S. Tazi, Integration of business process modeling and Web services: a survey, Serv. Oriented Comput. App. 8 (2014) 105–128. doi: 10.1007/s11761-013-0138-2
   Google Scholar
• X. Han, Y. Liang, Z. Li, G. Li, X. Wu, B. Wang, and G. Zhao, An Efficient Genetic Algorithm for Optimization Problems with Time-Consuming Fitness Evaluation. Int. J. Comp. Meth-Sing. 12 (2015). DOI: 10.1142/S0219876213501065.
   Google Scholar
• J. H. Holland, Adaptation in Natural and Artificial Systems, The MIT Press. 1975.
   Google Scholar
• N. Huber, A. Hoorn, A. Koziolek, F. Brosig, and S. Kounev, Modeling run-time adaptation at the system architecture level in dynamic service-oriented environments, Serv. Oriented Comput. App. 8 (2014) 73–89. doi: 10.1007/s11761-013-0144-4
   Google Scholar
• A. Immonen and D. Pakkala, A survey of methods and approaches for reliable dynamic service compositions, Serv. Oriented Comput. App. 8 (2014) 129–158. doi: 10.1007/s11761-013-0153-3
   Google Scholar
• G. D. Jason and G. M. Konstantinos, An experimental study of benchmarking functions for genetic algorithms. Int. J. Comput. Math. 79 (2002) 403–416. doi: 10.1080/00207160210939
   Web of Science ®Google Scholar
• D. H. Kim, and J. H. Cho, Advanced intelligence tuning using hybrid of clonal selection and genetic algorithm, GM and PM. Int. J. Comp. Intel. Appl. 14 (2015) doi: 10.1142/S1469026815500042.
   Web of Science ®Google Scholar
• T. Long, O. M. McDougal, and T. Andersen, GAMPMS: Genetic algorithm managed peptide mutant screening. J. Comput. Chem. 36 (2015) 1304–1310. doi: 10.1002/jcc.23928
   PubMed Web of Science ®Google Scholar
• N. H. Moin, O. C. Sin, and M. Omar, Hybrid Genetic Algorithm with Multiparents Crossover for Job Shop Scheduling Problems. Math. Problems Eng. (2015) doi: 10.1155/ 2015/ 210680.
   Web of Science ®Google Scholar
• B. R. Moon, Hybrid genetic algorithms with hyperplane synthesis: A theoretical and empirical study, PhD Thesis, The Pennsylvania State University, 1994.
   Google Scholar
• KSN. Ripon, S. Kwong, and K. F. Man, A real-coding jumping gene genetic algorithm (RJJGA) for multiobjective optimization, Inf. Sci. 177 (2007) 632–654. doi: 10.1016/j.ins.2006.07.019
   Web of Science ®Google Scholar
• J. P. Rosca, Towards automatic discovery of building blocks in genetic programming. In Working Notes for the AAAI Symp. Gene. Progr. 1995, 78–85.
   Google Scholar
• J. D. Schaffer, Learning Multiclass Pattern Discrimination, Proc. 1st Int. Conf. Gene. Algorithm. Lawrence Erlbaum Associates, 1985, 74–79.
   Google Scholar
• J. D. Schaffer and A. Morishima, An adaptive crossover distribution mechanism for genetic algorithms. Proc. 2ed Int. Conf. Gene. Algorithm. 1987, 36–40.
   Google Scholar
• M. Srinivas, and L. M. Patnaik, Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE T. Syst. Man CY. B. 24 (1994) 656–667. doi: 10.1109/21.286385
   Web of Science ®Google Scholar
• D. Thierens, Adaptive strategies for operator allocation. Parameter Setting in Evol. Algorithm. 54 (2007) 77–90. doi: 10.1007/978-3-540-69432-8_4
   Google Scholar
• C. F. Tsai, S. L. Lu, J. H. Chen, K. M. Chao, N. Shah, An adaptable optimizer for green component design, Inf. Syst. E-Bus. Manage. 13 (2015) 193–210. doi: 10.1007/s10257-014-0254-3
   Web of Science ®Google Scholar
• Y. W. Wang, An artificial chromosome embedded genetic algorithms for smart grid power demand forecast. Journal of Industrial and Intelligent Information 3 (2015) 69–74. doi: 10.12720/jiii.3.4.293-298
   Google Scholar