The analysis of the interaction of the migration, diversity and permeability in parallel genetic algorithms

Gultekin Kuvat; Nihat Adar

doi:10.11121/ijocta.01.2016.00262

Authors

Gultekin Kuvat Balikesir University
Nihat Adar Eskişehir Osmangazi University

DOI:

https://doi.org/10.11121/ijocta.01.2016.00262

Keywords:

Parallel genetic algorithms, migration, diversity, permeability

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.

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References

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). Crossref

Cantú-Paz, E., Markov chain models of parallel genetic algorithms, IEEE Transactions of Evolutionary Computation, 4(3), 216-226 (2000). Crossref

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). Crossref

Surry, P.D. and Radcliffe, N.J., RPL2: A language and parallel framework for evolutionary computing, Springer-Verlag LNCS 866, 628-637 (1994). Crossref

Alba, E. and Troya, J.M., A survey of parallel distributed genetic algorithms, Complexity 4, 31-52 (1999). Crossref

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). Crossref

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, Turkey (2007).

Kuvat, G., Adar, N., Canbek, S., Seke, E., Analysis the fast converging migration method for various test functions, 12th National Congress and Exhibition on Electrical, Electronics, Computer, Biomedical Engineering (in Turkish), 313-316, Eskişehir, Turkey (2007).

Kuvat, G., Adar, N., Canbek, S., Seke, E., Migration methods, migration rate and parallel genetic algorithms, ASYU 2008 - International Symposium on Innovations in Intelligent Systems and Applications (in Turkish), 138-142, Isparta, Turkey (2008).

Kuvat, G., Migration Methods and Dynamic Determination of Migration Parameters in Parallel Genetic Algorithms, Doctoral Dissertation (in Turkish), Department of Electrical and Electronics Engineering, Eskişehir Osmangazi University (2009).

Lazano, M., Herrera F. and Cano, J.R., Replacement strategies to preserve useful diversity in steady-state genetic algorithms, Information Sciences 178, 4421-4433 (2008). Crossref

Denzinger, J. and Kidney, J., Improving migration by diversity, The 2003 Congress on Evolutionary Computation, CEC'03, 1, 700- 707 (2003).

Li, Q. and Maeda, Y., Distributed adaptive search method for genetic algorithm controlled by fuzzy reasoning, IEEE International Conference on Fuzzy Systems, 2022-2027 (2008).

Adar, N. and Kuvat, G., Diversity and Permeability in Parallel Genetic Algorithms, Journal of the Institute of Science and Technology of Dumlupınar University, Num:27, 55-66, April 2012.

Goldberg, D.E., Genetic algorithms in search, optimization & machine learning, Addison Wesley Publishing Company, USA (1989).

Alba, E., Luna, F., Nebro A.J. and Troya, J.M., Parallel heterogeneous genetic algorithms for continuous optimization, Parallel Computing 30, 699-719 (2004). Crossref

Oh, S., Kim, C.T. and Lee, J., Balancing the selection pressures and migration schemes in parallel genetic algorithms for planning multiple paths, Proceedings of the 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea, May 21-26, 3314-3319 (2001).

Cantú-Paz., E., On the effects of migration on the fitness distribution of parallel evolutionary algorithms, In Workshop on Evolutionary Computation and Parallel Processing at GECCO-2000, 3-6 (2000).