Linked Genes Migration in Island Models

Marcin Komarnicki, Michal Przewozniczek


Island Models (IMs) divide the whole population into many coevolving subpopulations, which periodically exchange fractions of their individuals. Some IMs, exchange probabilistic models built during the subpopulations evolution. The use of many coevolving subpopulations helps to preserve the population diversity, which makes it less likely to get stuck in the local optima. Another promising research direction in the Evolutionary Computation field is the Linkage Learning. The knowledge about gene dependencies can be used in many different ways that improve the overall method effectiveness. Therefore, this paper proposes the Gene Pattern Based Island Model (GePIM) that uses the multi-population nature of IMs to generate the linkage information. GePIM also introduces a new type of migration based on exchanging linked gene groups, instead of exchanging the whole individuals or probabilistic models.


  1. Alves, H. N. 2015. A Multi-population Hybrid Algorithm to Solve Multi-objective Remote Switches Placement Problem in Distribution Networks. In Journal of Control, Automation and Electrical Systems, 25, 5, 545-555.
  2. Cai, Y., Wang, J. 2015. Differential evolution with hybrid linkage crossover. In Information Sciences, 320, 244- 287.
  3. Chang, W.D. 2015. A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems. In Applied Soft Computing, 33, 170-182.
  4. Chen, Y., Peng. W, Jian M. 2007a. Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery. In IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 37, 6, 1460-1470.
  5. Chen, Y., Sastry, K., Goldberg, D.E. 2007b. A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms. In IlliGAL Report No. 2007014, Illinois Genetic Algorithms Laboratory.
  6. Dahzi, W., Wu, C.H., Ip, W.H., Wang, D., Yan, Y. 2008. Parallel multi-population Particle Swarm Optimization Algorithm for the Uncapacitated Facility Location problem using OpenMP. In IEEE Congress on Evolutionary Computation, 124-128.
  7. delaOssa, L., Gámez, J.A., Puerta, J.M. 2004. Migration of Probability Models Instead of Individuals: An Alternative When Applying the Island Model to EDAs. In Lecture Notes in Computer Science (PPSN 2004), 3242, 242-252.
  8. Fidrysiak, B., Przewozniczek, M. 2015. Towards Finding an Effective Way of Discrete Problems Solving: the Particle Swarm Optimization, Genetic Algorithm and Linkage Learning Techniques Hybrydization. In Proceedings of the 7th International Joint Conference on Computational Intelligence, 228-236, DOI=10.5220/000559660228023.
  9. Fieldsend, J. E. 2014. Running Up Those Hills: MultiModal Search with the Niching Migratory MultiSwarm Optimiser. In IEEE Congress on Evolutionary Computation, 2593-2600.
  10. Goldberg, D.E., Deb, K., Kargupta, H., Harik, G. 1993. Rapid, Accurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms. In Prcs. 5th International Conference on Genetic Algorithms, 55- 64.
  11. Kim, H.H., Choi, J.Y. 2015. Pattern generation for multiclass LAD using iterative genetic algorithm with flexible chromosomes and multiple populations. In Expert Systems with Applications, 42, 833-843.
  12. Kurdi, M. 2016. An effective new island model genetic algorithm for job shop scheduling problem. In Computers and Operations Research, 67, 132-142.
  13. Kwasnicka, H., Przewozniczek, M. 2011. Multi Population Pattern Searching Algorithm: a new evolutionary method based on the idea of messy Genetic Algorithm. In IEEE Transactions on evolutionary computation, 15, 5, 715-734.
  14. Leitão, A., Pereira, F.B., Machado, P. 2015. Island models for cluster geometry optimization: how design options impact effectiveness and diversity. In Journal of Global Optimization, 63, 677-707.
  15. Muelas, S., Mendiburu, A., LaTorre, A., Peña, J.-M. 2014. Distributed Estimation of Distribution Algorithms for continuous optimization: How does the exchanged information influence their behavior? In Information Sciences, 268, 231-254.
  16. Omidivar, M.N., Li, X., Mei, Y., Yao, X. 2014. Cooperative Co-evolution with Differential Grouping for Large Scale Optimization. In IEEE Transactions on evolutionary computation, 18, 378-393.
  17. Pisinger, D. 2005. Where are the hard knapsack problems? In Compuers and Operation Research. 32, 9, 2271- 2284.
  18. DOI=
  19. Pelikan, M., Goldberg, D.E., Cantu-Paz, E. 1999. BOA: The Bayesian Optimization Algorithm. In IlliGAL Report No. 99003.
  20. Pelikan, M., Sastry, K., Butz, M.V., Goldberg, D.E. 2006. Hierarchical BOA on Random Decomposable Problems. In MEDAL Report No. 2006001.
  21. Przewozniczek, M., Goscien, R., Walkowiak, K., Klinkowski, M. 2015. Towards Solving Practical Problems of Large Solution Space Using a Novel Pattern Searching Hybrid Evolutionary Algorithm - An Elastic Optical Network Optimization Case Study. In Expert Systems with Applications, 42, 7781-7796.
  22. Przewozniczek, M. 2015. Towards finding an effective uniform and single point crossover balance for optimization of Elastic Optical Networks. In The Proceedings Of The Second European Network Intelligence Conference.
  23. Przewozniczek, M., 2016. Active Multi Population Pattern Searching Algorithm for Flow Optimization in Computer Networks - the novel coevolution schema combined with linkage learning. In Information Sciences, 355-356, 15-36.
  24. Saha, A., Datta, R., Deb, K. 2010. Hybrid gradient projection based Genetic Algorithms for constrained optimization. In IEEE Congress on Evolutionary Computation, 1-8.
  25. Skolicki, Z., De Jong, K. 2007. The importance of a twolevel perspective for island model design. In Proceedings of the IEEE Congress on Evolutionary Computation, 4623-4630.
  26. Skolicki, Z. 2008. Linkage in Island Models. In Lecture Notes in Computer Science, 157, 41-60.
  27. Suganthan, P., Hansen, N., Liang, J., Deb, K., Chen, Y., Auger, A., Tiwari, S. 2005. Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. In Technical Report, 1- 50, Nanyang Technol Universiy, Singapore.
  28. Walkowiak, K., Przewozniczek, M., Pajak, K. 2013. Heuristic Algorithms for Survivable P2P Multicasting. In Applied Artificial Intelligence, 27, 4, 278-303.
  29. Wang, J., Zhang, W., Zhang, J. 2015. Cooperative Differential Evolution With Multiple Populations for Multiobjective Optimization. In IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2015.2490669 (in press).
  30. Watanabe, O., Yamamoto, M. 2010. Average-case analysis for the MAX-2SAT problem. In Theoretical Computer Science, 411, 1685-1697. DOI=
  31. Watson, R.A., Pollack, J.B. 1999. Hierarchically Consistent Test Problems for Genetic Algorithms. In Proceedings of 1999 Congress on Evolutionary Computation, CEC-99, 2.
  32. Watson, R.A. 2006. Compositional Evolution: The impact of Sex, Symbiosis and Modularity on the Gradualist Framework of Evolution. In Vienna Series in Theoretical Biology. MIT Press.
  33. Yang, Z., Tang, K., Yao, X. 2008. Large scale evolutionary optimization using cooperative coevolution. In Information Sciences, 178, 2986-2999.
  34. Yu, T., Goldberg, D.E., Sastry, K., Lima, C.F., Pelikan, M. 2009. Dependency structure matrix, genetic algorithms, and effective recombination. In Evolutionary Computation, 17, 595-626.
  35. Zavoianu, A.-C., Lughofer, E., Bramerdorfer, G., Amrhein, W., Klemen, E. P. 2015. DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm. In Soft Computing, 19, 12, 3551-3569.
  36. Zhang, J., Huang, D.-S., Liu, K.-H. 2007. Multi-SubSwarm Optimization Algorithm for Multimodal Function Optimization. In IEEE Congress on Evolutionary Computation, 3215-3220.

Paper Citation

in Harvard Style

Komarnicki M. and Przewozniczek M. (2016). Linked Genes Migration in Island Models . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 30-40. DOI: 10.5220/0006042300300040

in Bibtex Style

author={Marcin Komarnicki and Michal Przewozniczek},
title={Linked Genes Migration in Island Models},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)},

in EndNote Style

JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)
TI - Linked Genes Migration in Island Models
SN - 978-989-758-201-1
AU - Komarnicki M.
AU - Przewozniczek M.
PY - 2016
SP - 30
EP - 40
DO - 10.5220/0006042300300040