In addition to this, for the different migration parameters of the individuals that will migrate the values of diversity and permeability have been calculated and the obtained results have been analysed.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:Diversity is an important factor for genetic algorithms to do a successful search.
📊
Key Findings & Data:In this study, the effect of changing migration intervals on the permeability and the performance of algorithm has been examined and it has been showed that the migration done in exploration phase has made much more contribution to the performance of the algorithm.
💡
Conclusion & Impact:In addition to this, for the different migration parameters of the individuals that will migrate the values of diversity and permeability have been calculated and the obtained results have been analysed.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
Diversity is an important factor for genetic algorithms to do a successful search. High diversity obtained owing to the migration is one of the most important reasons of generation of successful results by Parallel Genetic Algorithms. While permeability is a new term that analyses the effect of the migration parameters in Parallel Genetic Algorithms. In this study, the effect of changing migration intervals on the permeability and the performance of algorithm has been examined and it has been showed that the migration done in exploration phase has made much more contribution to the performance of the algorithm. In addition to this, for the different migration parameters of the individuals that will migrate the values of diversity and permeability have been calculated and the obtained results have been analysed.
🔖
Cite This Article
KUVATA, G., ADARB, N. (2022). The analysis of the interaction of the migration, diversity and permeability in parallel genetic algorithms. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 6 - Sayı: 1, 23–32. https://ajindex.com/articles/440704
Rebaudengo, M. and Reorda, M.S., An experimental analysis of effects of migration in parallel genetic algorithms, EWPDP93: IEEE/Euromicro Workshop on Parallel and Distributed Processing, Gran Canaria (E), Gennaio, 232-238 (1992)
Alba E. and Troya, J.M., Analyzing synchronous and asynchronous parallel distributed genetic algorithms, Future Generation Computer Systems 17, 451-465 (2001)
Cantú-Paz, E., Markov chain models of parallel genetic algorithms, IEEE Transactions of Evolutionary Computation, 4(3), 216-226 (2000)
Cantú-Paz, E., Migration policies, selection pressure, and parallel evolutionary algorithms, In Brave, S., Wu, A. (Eds.) Late Breaking Papers at the Genetic and Evolutionary Computation Conference. Orlando, FL (1999)
Cantú-Paz, E., Topologies, migration rates, and multi-population parallel genetic algorithms, GECCO-99:Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco, CA: Morgan Kaufmann, 91-98 (1999)
Nowostawski, M. and Poli, R., Parallel genetic algorithm taxonomy, Proceedings of Third International Conference on Knowledge-based Intelligent Information Engineering Systems KES'99 (1999)
Surry, P.D. and Radcliffe, N.J., RPL2: A language and parallel framework for evolutionary computing, Springer-Verlag LNCS 866, 628-637 (1994)
Alba, E. and Troya, J.M., A survey of parallel distributed genetic algorithms, Complexity 4, 31-52 (1999)
Maeda, Y., Ishita, M.and Li, O., Fuzzy adaptive search method for parallel genetic algorithm with island combination process, International Journal of Approximate Reasoning 41, 59-73 (2006)
Hiroyasu, T., Miki, M. and Negami, M., Distributed genetic algorithms with randomized migration rate, IEEE Proc. of Systems, Man and Cybernetics Conference (SMC'99), 1, 689-694 (1999)
Berntsson, J. and Tang, M., A convergence model for asynchronous parallel genetic algorithms, The 2003 Congress on Evolutionary Computation, CEC'03, 4, 2627- 2634 (2003)
Kuvat, G., Adar, N., Canbek, S., Seke, E., Fast converging migration method in Parallel Genetic Algorithms, UMES 2007-National Young Researchers Symposium on Technical Training, Engineering and Education Sciences (in Turkish), 71-74, Kocaeli, Turkey, June (2007)
Kuvat, G., Adar, N., Seke, E., Canbek, S., New migration schemes for parallel genetic algorithms on cluster systems, INISTA 2007- International Symposium on Innovations in Intelligent Systems and Applications, 268-272, Istanbul