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Semantic Evolutionary Visualization

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Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10386))

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Abstract

The Evolutionary optimization (EO) field has become an active area of research for handling complex optimization problems. However, EO techniques need tuning to obtain better results. Visualization is one approach used by EA researchers to identify early stagnation, loss of diversity, and other indicators that can help them to guide evolutionary search to better areas. In this paper, a Semantic Evolutionary Visualization framework (SEV) is proposed for analysing and exploring the potential EA dynamics. Empirical results have shown that the SEV can help to reveal and monitor information on evolutionary dynamics; thus, it can assist researchers in adapting the evolutionary parameters to obtain better performance.

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References

  1. Guliashki, V., Toshev, H., Korsemov, C.: Survey of evolutionary algorithms used in multiobjective optimization. Prob. Eng. Cybern. Rob. 60, 42–54 (2009)

    MathSciNet  Google Scholar 

  2. Peltonen, T.: Comparative study of population-based metaheuristic methods in global optimization (2015)

    Google Scholar 

  3. McClymont, K., Keedwell, E., Savic, D.: An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design. Env. Model. Softw. (2015)

    Google Scholar 

  4. Tušar, T., Filipič, B.: Visualization of pareto front approximations in evolutionary multiobjective optimization: a critical review and the prosection method. IEEE Trans. Evol. Comput. 19(2), 225–245 (2015)

    Article  Google Scholar 

  5. He, Z., Yen, G.G.: Visualization and performance metric in many-objective optimization. IEEE Trans. Evol. Comput. 20(3), 386–402 (2016)

    Article  Google Scholar 

  6. Meyer, J., Thomas, J., Diehl, S., Fisher, B.D., Keim, D.A., Laidlaw, D.H., Miksch, S., Mueller, K., Ribarsky, W., Preim, B., et al.: From visualization to visually enabled reasoning. Sci. Vis. Adv. Concepts 1, 227–245 (2010)

    Google Scholar 

  7. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)

    Article  Google Scholar 

  8. Collins, T.D.: Visualizing evolutionary computation. In: Advances in Evolutionary Computing, pp. 95–116. Springer, Heidelberg (2003)

    Google Scholar 

  9. Paterson, T., Graham, M., Kennedy, J., Law, A.: Evaluating the viper pedigree visualisation: detecting inheritance inconsistencies in genotyped pedigrees. In: 2011 IEEE Symposium on Biological Data Visualization (BioVis), pp. 119–126. IEEE (2011)

    Google Scholar 

  10. Collins, T.D.: Using software visualisation technology to help evolutionary algorithm users validate their solutions. In: ICGA, pp. 307–314. Citeseer (1997)

    Google Scholar 

  11. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  12. Thangaraj, R., Pant, M., Abraham, A., Badr, Y.: Hybrid evolutionary algorithm for solving global optimization problems. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 310–318. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02319-4_37

    Chapter  Google Scholar 

  13. Cohen, J., Cohen, P., West, S.G., Aiken, L.S.: Applied Multiple Regression/correlation Analysis for the Behavioral Sciences. Routledge, New York (2013)

    Google Scholar 

  14. De Nooy, W., Mrvar, A., Batagelj, V.: Exploratory Social Network Analysis with Pajek, vol. 27. Cambridge University Press, Cambridge (2011)

    Book  Google Scholar 

  15. Romero, G., Merelo, J.J., Castillo, P.A., Castellano, J.G., Arenas, M.G.: Genetic algorithm visualization using self-organizing maps. In: Guervós, J.J.M., Adamidis, P., Beyer, H.-G., Schwefel, H.-P., Fernández-Villacañas, J.-L. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 442–451. Springer, Heidelberg (2002). doi:10.1007/3-540-45712-7_43

    Google Scholar 

  16. Amir, E.-A.D., Davis, K.L., Tadmor, M.D., Simonds, E.F., Levine, J.H., Bendall, S.C., Shenfeld, D.K., Krishnaswamy, S., Nolan, G.P., Pe’er, D.: visne enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31(6), 545–552 (2013)

    Article  Google Scholar 

  17. Jornod, G., Di Mario, E.L., Navarro Oiza, I., Martinoli, A.: Swarmviz: an open-source visualization tool for particle swarm optimization. In: IEEE Congress on Evolutionary Computation, no. EPFL-CONF-206841 (2015)

    Google Scholar 

  18. de Freitas, A.R., Fleming, P.J., Guimarães, F.G.: Aggregation trees for visualization and dimension reduction in many-objective optimization. Inf. Sci. 298, 288–314 (2015)

    Article  Google Scholar 

  19. Khemka, N., Jacob, C.: Visplore: a toolkit to explore particle swarms by visual inspection. In: Proceedings of the 11th Annual conference on Genetic and Evolutionary Computation, pp. 41–48. ACM (2009)

    Google Scholar 

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Acknowledgments

I would to acknowledge prof. Hussein Abbass for supporting this work.

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Authors and Affiliations

  1. University of New South Wales Canberra, Canberra, Australia

    Marwa Keshk

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  1. Marwa Keshk
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Correspondence to Marwa Keshk .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  4. Shenzhen University, Shenzhen, China

    Ben Niu

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Keshk, M. (2017). Semantic Evolutionary Visualization. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_66

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  • DOI: https://doi.org/10.1007/978-3-319-61833-3_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61832-6

  • Online ISBN: 978-3-319-61833-3

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