Evolutionary visual exploration: Evaluation of an IEC framework for guided visual search

Evolutionary Computation

Volumn 25, Issue 1, 2017, Pages 55-86

  Boukhelifa, N ^a   Bezerianos, A ^b   Cancino, W ^a   Lutton, E ^c  

^a INRIA   (France)

^b CNRS   (France)

^c CEMAGREF   (France)

Author keywords

Data mining; Genetic programming; Information visualization; Interactive evolutionary algorithms; Interactive evolutionary computation; Visual analytics

Indexed keywords

DATA MINING; DATA VISUALIZATION; GENETIC ALGORITHMS; GENETIC PROGRAMMING; INFORMATION SYSTEMS; SOFTWARE PROTOTYPING; VISUALIZATION;

EXPLORATION STRATEGIES; INFORMATION VISUALIZATION; INTERACTIVE EVOLUTION; INTERACTIVE EVOLUTIONARY ALGORITHMS; INTERACTIVE EVOLUTIONARY COMPUTATION; MULTI-DIMENSIONAL DATASETS; TWO-DIMENSIONAL PROJECTION; VISUAL ANALYTICS;

EVOLUTIONARY ALGORITHMS;

ADULT; ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; EVALUATION STUDY; EXPERT WITNESS; FEMALE; HUMAN; MALE; MARKOV CHAIN; PATTERN RECOGNITION; PROCEDURES; STATISTICAL ANALYSIS; STATISTICS AND NUMERICAL DATA; THEORETICAL MODEL;

ADULT; ALGORITHMS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; EXPERT TESTIMONY; FEMALE; HUMANS; MALE; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED; PATTERN RECOGNITION, VISUAL; STOCHASTIC PROCESSES;

EID: 85014475316     PISSN: 10636560     EISSN: 15309304     Source Type: Journal    
DOI: 10.1162/EVCO_a_00161     Document Type: Article

References (55)

1. Albuquerque, G., Eisemann, M., and Magnor, M. (2011). Perception-based visual quality measures. In Proceedings of IEEE Symposium on Visual Analytics Science and Technology, pp. 13–20.
2. Banzhaf, W. (1997). Interactive evolution. In T. Back, D. Fogel, and Z.Michalewicz (Eds.), Handbook of Evolutionary Computation, pp. C2.9.1–C2.9.6. Oxford: Oxford University Press.
3. Behrisch, M., Korkmaz, F., Shao, L., and Schreck, T. (2014). Feedback-driven interactive exploration of large multidimensional data supported by visual classifier. In Proceedings of IEEE Symposium on Visual Analytics Science and Technology, pp. 43–52.
4. Bertini, E., Tatu, A., and Keim, D. (2011). Quality metrics in high-dimensional data visualization: An overview and systematization. IEEE Transactions on Visualization and Computer Graphics, 17 (12): 2203–2212.
5. Bezerianos, A., Chevalier, F., Dragicevic, P., Elmqvist, N., and Fekete, J.-D. (2010). GraphDice: A system for exploring multivariate social networks. In Proceedings of Eurographics Conference on Visualization, pp. 863–872.
6. Boukhelifa, N., Cancino, W., Bezerianos, A., and Lutton, E. (2013). Evolutionary visual exploration: Evaluation with expert users. In Proceedings of Eurographics Conference on Visualization, pp. 31–40.
7. Brown, E. T., Liu, J., Brodley, C. E., andChang, R. (2012). Dis-function: Learning distance functions interactively. In Proceedings of IEEE conference on Visual Analytics Science and Technology, pp. 83–92.
8. Brown, E. T., Ottley, A., Zhao, H., Lin, Q., Souvenir, R., Endert, A., and Chang, R. (2014). Finding Waldo: Learning about users from their interactions. IEEE Transactions on Visualization and Computer Graphics, 20 (12): 1663–1672.
9. Card, S.K., Mackinlay, J. D., and Shneiderman, B. (Eds.) (1999). Readings in information visualization: Using vision to think. San Francisco: Morgan Kaufmann.
10. Carpendale, S. (2008). Evaluating information visualizations. In A. Kerren, J. Stasko, J. Fekete, and C. North (Eds.), Information visualization, pp. 19–45. Berlin: Springer.
11. Chen, C. (2005). Top 10 unsolved information visualization problems. Computer Graphics and Applications, 25 (4): 12–16.
12. Chen, M., and Hagen, H. (2010). Guest editors’ introduction: Knowledge-assisted visualization. Computer Graphics and Applications, 30 (1): 15–16.
13. Dang, T. N., and Wilkinson, L. (2014). Scagexplorer: Exploring scatterplots by their scagnostics. In Proceedings of IEEE Pacific Visualization Symposium, pp. 73–80.
14. Elmqvist, N., Dragicevic, P., and Fekete, J.-D. (2008). Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1141–1148.
15. Endert, A., Fiaux, P., and North, C. (2011). Unifying the sensemaking loop with semantic interaction. In Proceedings of IEEE Workshop on Interactive Visual Text Analytics for Decision Making, VisWeek.
16. Endert, A., Fiaux, P., and North, C. (2012). Semantic interaction for visual text analytics. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pp. 473–482.
17. Fernstad, S. J., Shaw, J., and Johansson, J. (2013). Quality-based guidance for exploratory dimensionality reduction. Information Visualization, 12 (1): 44–64.
18. Fischer, G. (2001). User modeling in human and computer interaction. User Modeling and User-Adapted Interaction, 11 (1-2): 65–86.
19. Fukumoto, M., Ogawa, S., Nakashima, S., and Ichi Imai, J. (2010). Extended interactive evolutionary computation using heart rate variability as fitness value for composing music chord progression. In Proceedings of World Congress on Nature and Biologically Inspired Computing, pp. 407–412.
20. Grinstein, G. G. (1996). Harnessing the human in knowledge discovery. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 384–385.
21. Guettala, A., Bouali, F., Guinot, C., and Venturini, G. (2012). A user assistant for the selection and parameterization of the visualizations in visual data mining. In Proceedings of International Conference on Information Visualisation, pp. 252–257.
22. Hayashida, N., and Takagi, H. (2000). Visualized IEC: Interactive evolutionary computation with multidimensional data visualization. In Proceedings of Conference of the IEEE Industrial Electronics Society, pp. 2738–2743.
23. Ingram, S., Munzner, T., Irvine, V., Tory, M., Bergner, S., and Moller, T. (2010). DimStiller: Workflows for dimensional analysis and reduction. In Proceedings of IEEE Symposium on Visual Analytics Science and Technology, pp. 3–10.
24. Johansson, S., and Johansson, J. (2009). Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 993–1000.
25. Koza, J. R. (1992). Genetic programming. Cambridge, MA: MIT Press.
26. Leman, S., House, L., Maiti, D., Endert, A., and North, C. (2013). Visual to parametric interaction (v2pi). PLoS ONE, 8: e50474.
27. Llora, X., Sastry, K., Alıas, F., Goldberg, D. E., and Welge, M. (2006). Analyzing active interactive genetic algorithms using visual analytics. In Proceedings of Conference on Genetic and Evolutionary Computation (GECCO), pp. 1417–1418.
28. Lutton, E. (2006). Evolution of fractal shapes for artists and designers. International Journal of Artificial Intelligence Tools, 15 (4): 651–672.
29. Lutton, E., Pilz, M., and Lévy Véhel, J. (2005). The fitness map scheme: Application to interactive multifractal image denoising. In Proceedings of IEEE Congress on Evolutionary Computation, pp. 2278–2285.
30. Mackay, W. E. (2000). Responding to cognitive overhead: Co-adaptation between users and technology. Intellectica, 30 (1): 177–193.
31. Malinchik, S., and Bonabeau, E. (2004). Exploratory data analysis with interactive evolution. In Proceedings of Conference on Genetic and Evolutionary Computation (GECCO), 1151–1161.
32. Matkovic, K., Gracanin, D., Jelovic, M., and Hauser, H. (2008). Interactive visual steering rapid visual prototyping of a common rail injection system. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1699–1706.
33. Matkovic, K., Gracanin, D., Jelovic, M., and Hauser, H. (2011). Interactive visual analysis supporting design, tuning, and optimization of diesel engine injection. In Proceedings of IEEE Visualization Conference (Discovery Exhibition).
34. Mengshoel, O. J., and Goldberg, D. E. (2008). The crowding approach to niching in genetic algorithms. Evolutionary Computation, 16 (3): 315–354.
35. Meyer, M., Sedlmair, M., and Munzner, T. (2012). The four-level nested model revisited: Blocks and guidelines. In Proceedings of Workshop on Novel Evaluation Methods for Information Visualization, article 11.
36. Mouradian, J. A., Hamann, B., and Rosenbaum, R. (2012). Ageneral approach for similarity-based linear projections using a genetic algorithm. In Proceedings of SPIE, 8294: 8294OL.
37. Nam, J. E., and Mueller, K. (2013). TripadvisorN−D: A tourism-inspired high-dimensional space exploration framework with overview and detail. IEEE Transactions on Visualization and Computer Graphics, 19 (2): 291–305.
38. Peng, W., Ward, M. O., and Rundensteiner, E. A. (2004). Clutter reduction in multi-dimensional data visualization using dimension reordering. In Proceedings of the IEEE Symposium on Information Visualization, pp. 89–96.
39. Perer, A., and Shneiderman, B. (2009). Integrating statistics and visualization for exploratory power: From long-term case studies to design guidelines. Computer Graphics and Applications, 29 (3): 39–51.
40. Pham, T., Hess, R., Ju, C., Zhang, E., and Metoyer, R. A. (2010). Visualization of diversity in large multivariate data sets. IEEE Transactions on Visualization and Computer Graphics, 16 (6): 1053–1062.
41. Poli, R., and Cagnoni, S. (1997). Genetic programming with user-driven selection: Experiments on the evolution of algorithms for image enhancement. In Proceedings of the Conference on Genetic Programming, pp. 269–277.
42. Rensink, R. A., and Baldridge, G. (2010). The perception of correlation in scatterplots. Computer Graphics Forum, 29 (3): 1203–1210.
43. Saraiya, P., North, C., and Duca, K. (2005). An insight-based methodology for evaluating bioinformatics visualizations. IEEE Transactions on Visualization and Computer Graphics, 11 (4): 443–456.
44. Sedlmair, M., Heinzl, C., Bruckner, S., Piringer, H., and Moller, T. (2014). Visual parameter space analysis: A conceptual framework. IEEE Transactions on Visualization and Computer Graphics, 20 (12): 2161–2170.
45. Sedlmair, M., Meyer, M., and Munzner, T. (2012). Design study methodology: Reflections from the trenches and the stacks. IEEE Transactions on Visualization and Computer Graphics, 18 (12): 2431–2440.
46. Seo, J., and Shneiderman, B. (2005). Rank-by-feature framework for interactive exploration of multidimensional data. Information Visualization, 4 (2): 99–113.
47. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27: 379–423, 623–656.
48. Shao, L., Behrisch, M., Schreck, T., von Landesberger, T., Scherer, M., Bremm, S., and Keim, D. A. (2014). Guided sketching for visual search and exploration in large scatterplot spaces. In Proceedings of EuroVAInternationalWorkshop onVisualAnalytics. doi: 10-2312/eurova.20141140
49. Smith, I. (2002). A tutorial on principal component analysis. Retrieved from http://www.cs. otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
50. Stolper, C. D., Perer, A., and Gotz, D. (2014). Progressive visual analytics: User-driven visual exploration of in-progress analytics. IEEE Transactions on Visualization and Computer Graphics, 20 (12): 1653–1662.
51. Takagi, H. (1998). Interactive evolutionary computation: System optimisation based on human subjective evaluation. In Proceedings of IEEE International Conference on Intelligent Engineering Systems, pp. 1–6.
52. Thomas, J. J., and Cook, K. A. (2005). Illuminating the path: Research and development agenda for visual analytics. National Visualization and Analytics Center. Retrieved from vis.pnnl.gov/pdf/RD_Agenda_VisualAnalytics.pdf
53. Cancino, W., Boukhelifa, N., Bezerianos, A., and Lutton, E. (2013). Evolutionary visual exploration: Experimental analysis of algorithm behaviour. In Proceedings of Conference Companion on Genetic and Evolutionary Computation, pp. 1373–1380.
54. Turkay, C., Jeanquartier, F., Holzinger, A., and Hauser, H. (2014). On computationally-enhanced visual analysis of heterogeneous data and its application in biomedical informatics. In A. Holzinger and I. Jurisica (Eds.), Interactive knowledge discovery and data mining in biomedical informatics, pp. 117–140. Berlin: Springer.
55. Wilkinson, L., andWills, G. (2008). Scagnostics distributions. Journal of Computational andGraphical Statistics, 17 (2): 473–491.