Influence of Migration on Efficacy and Efficiency of Parallel Evolutionary Computing

[1] L. D. Davis, K. De Jong, M. D. Vose, L. D. Whitley, W. Miller, Eds., Evolutionary Algorithms, Vol. 111 of The IMA Volumes in Mathematics and its Applications, New York, NY, Springer, 1999, [Online]. Available: doi: 10.1007/978-1-4612-1542-4. Search in Google Scholar

[2] Z. Skolicki, K. De Jong, The inϐluence of migration intervals on island models, 2005, pp. 1295–1302, doi: 10.1145/1068009.1068219. Search in Google Scholar

[3] D. Wolpert, W. Macready, “No Free Lunch Theorems for Optimization”, IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, 1997. Search in Google Scholar

[4] D. Sudholt, Parallel Evolutionary Algorithms, Berlin, Heidelberg, Springer Handbooks, Springer, 2015, doi: 10.1007/978-3-662-43505-2-46. Search in Google Scholar

[5] E. Cantu-Paz, “On the Effects of Migration on the Fitness Distribution of Parallel Evolutionary Algorithms”, no. UCRL-JC-138729, 2000, URL ht tps://www.osti.gov/biblio/791479. Search in Google Scholar

[6] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison- Wesley, 1989, google-Books-ID: 2IIJAAAACAAJ. Search in Google Scholar

[7] R. Chiong, T. Weise, Z. Michalewicz, Variants of Evolutionary Algorithms for Real-World Applications, 2012, doi: 10.1007/978-3-642-23424-8. Search in Google Scholar

[8] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer Science & Business Media, 1996, google-Books-ID: vlh-LAobsK68C. Search in Google Scholar

[9] E. Cantú-Paz, Master-Slave Parallel Genetic Algorithms, Genetic Algorithms and Evolutionary Computation, Boston, MA, Springer, 2001, doi: 10.1007/978-1-4615-4369-5-3. Search in Google Scholar

[10] E. Cantú-Paz, Fine-Grained and Hierarchical Parallel Genetic Algorithms, Genetic Algorithms and Evolutionary Computation, Boston, MA, Springer, 2001, doi: 10.1007/978-1-4615-43 69-5-8. Search in Google Scholar

[11] E. Cantú-Paz, D. E. Goldberg, “Efϐicient parallel genetic algorithms: theory and practice”, Computer Methods in Applied Mechanics and Engineering, vol. 186, no. 2–4, pp. 221–238, 2000, doi: 10.1016/S0045-7825(99)00385-0. Search in Google Scholar

[12] Y. Sato, Y. Takai, M. Munetomo, “An efϐicient migration scheme for subpopulation-based asynchronously parallel genetic algorithms.”, Material: Proceedings of the ϐifth international conference on genetic algorithms, S.F. Ed., San Mateo, CA: Morgan Kaufmann, 1993, pp. 649. Search in Google Scholar

[13] H. Braun, “On solving travelling salesman problems by genetic algorithms”, Parallel Problem Solving from Nature, Lecture Notes in Computer Science, H.P. Schwefel and R. Manner, Eds. Berlin, Heidelberg: Springer, 1991, pp. 129–133, doi: 10 .1007/BFb0029743. Search in Google Scholar

[14] E. Cantú-Paz, D. E. Goldberg, On the Scalability of Parallel Genetic Algorithms, vol. 7, 1999, pp. 429–449, doi: 10.1162/evco.1999.7.4.429. Search in Google Scholar

[15] M. Nowostawski, R. Poli, “Parallel genetic algorithm taxonomy”, 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Proceedings (Cat. No.99TH8410), 1999, pp. 88-92, doi: 10.1109/ KES.1999.820127. Search in Google Scholar

[16] M. Ruciński, D. Izzo, F. Biscani, “On the impact of the migration topology on the island model”, Parallel Computing, Issues, vol. 36, no. 10–11, pp. 555–571, 1993, doi: 10.1016/j.parco.2010.04.002. Search in Google Scholar

[17] D. Whitley, S. Rana, R. Heckendorn, “The Island Model Genetic Algorithm: On Separability, Population Size and Convergence”, Journal of Computing and Information Technology, vol. 7, Dec. 1998. Search in Google Scholar

[18] Z. Skolicki, K. De Jong, “The importance of a two-level perspective for island model design”, Conference: Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, IEEE Xplore, 2007, doi: 10.1109/CEC.2007.4425078. Search in Google Scholar