1. Overview of IGA Application and Combating User Fatigue Jie-Wei Wu 2013/3/12

2. Agenda Different Applications Using IGA – S.-B. Cho and J.-Y. Lee, “A human-oriented image retrieval system using interactive genetic algorithm,” IEEE Trans. Syst., Man, Cybern. A, vol. 32, pp. 452–458, May 1998. – Kim, H.-S. and Cho, S.-B. (2000), “Application of interactive generic algorithm to fashion design”, Engineering Applications of Artificial Intelligence, Vol. 13, pp. 635-44.

3. Agenda Llorà, X., Sastry, K., Goldberg, D. E., Gupta, A., and Lakshmi, L. (2005). Combating user fatigue in iGAs: Partial ordering, support vector ma-chines, and synthetic fitness. Proceedings of the Genetic and Evolutionary Computation Conference, pages 1363-1370.

4. Agenda Different Applications Using IGA – Kim, H.-S. and Cho, S.-B. (2000), “Application of interactive generic algorithm to fashion design”, Engineering Applications of Artificial Intelligence, Vol. 13, pp. 635-44. – S.-B. Cho and J.-Y. Lee, “A human-oriented image retrieval system using interactive genetic algorithm,” IEEE Trans. Syst., Man, Cybern. A, vol. 32, pp. 452–458, May 1998.

5. Motivation As most consumers are not professional at design, however, a sophisticated computer- aided design system might be helpful to choose and order what they want.

10. Experimental results Population size is 8. The number of generation is limited to 10. One-point XO One elitist individual in each generation Convergence test and subject test

11. Convergence Test 10 subjects are requested to find cool-looking design and splendid design using the system. The meaning of ‘splendid’ might be more complex and various than that of ‘cool- looking.’

12. Subject Test Randomly selected 500 sample designs from entire search space, and requested 3 subjects to evaluate the sample designs with two categories, coolness and splendor. 10 most cool-looking designs and another 10 most splendid ones are selected as standards of evaluation.

13. Subject Test Request 10 subjects to find cool-looking design and splendid design using the system. Each searching is limited to 10 generations.

14. Subject Test This score has 7 degrees (from -3 to 3).

15. Conclusion of This Paper An IGA-based fashion design aid system for non-professionals. A more realistic and reasonable design in OpenGL design model. Future : the search space should be enlarged.(original 1,880,064)

16. Different Applications Using IGA – Kim, H.-S. and Cho, S.-B. (2000), “Application of interactive generic algorithm to fashion design”, Engineering Applications of Artificial Intelligence, Vol. 13, pp. 635-44. – S.-B. Cho and J.-Y. Lee, “A human-oriented image retrieval system using interactive genetic algorithm,” IEEE Trans. Syst., Man, Cybern. A, vol. 32, pp. 452–458, May 1998.

17. Motivation Most of the conventional methods lack of the capability to utilize human intuition and emotion appropriately in the process of retrieval. It is difficult to retrieve a satisfactory result when the user wants an image that cannot be explicitly specified because it deals with emotion.

18. Discrete Wavelet Transform Construct a matrix of coefficient values. Haar Wavelet Transform Only the largest 50 coefficients in RGB channels.

19. GA Operators Population size is 12. Horizontal and vertical crossovers

21. Search The similarity between potential target image and candidate image is calculated. 12 images of higher magnitude value are provided as a result of the search.

22. Experimental Results Convergence Test Efficiency Test Psychological Test

23. Convergence Test “We can see that there are more images of gloomy mood in the eighth generation than those in the beginning.” ?

24. Efficiency Test Request 10 graduate students to search gloomy and splendid images and ask how similar the result image is to what they have in mind.

25. Psychological Test

27. Conclusion of This Paper An approach that searches an image with human preference and emotion using GA. To search not only an explicitly expressed image, but also an abstract image such as “cheerful impression image,” “gloomy impression image,” and so on.

28. Combating User Fatigue in iGAs: Partial ordering, Support Vector Machines, and Synthetic Fitness Llorà, X., Sastry, K., Goldberg, D. E., Gupta, A., and Lakshmi, L. (2005). Proceedings of the Genetic and Evolutionary Computation Conference, pages 1363-1370.

29. Motivation One of the daunting challenges of interactive genetic algorithms (iGAs) is user fatigue which leads to sub-optimal solutions. Combating the user fatigue problem of iGAs: – The lack of a computable fitness – How synthetic fitness models based on user evaluation may be built.

30. Components of Proposed Method Partial Ordering: The qualitative decisions made by the user about relative solution quality is used to generate partial ordering of solutions Induced Complete Order: The concepts of non- domination and domination count from multi-objective evolutionary algorithms to induce a complete order of the solutions in the population based on their partial ordering Surrogate Function via SVM: The induced order is used to assign ranks to the solutions and use them in a support vector machine (SVM) to create a surrogate fitness function that effectively models user fitness.

31. Elements IGAs Need to Address Clear goal definition Impact of problem visualization Lack of real fitness Fatigue Persistence of user criteria

32. Synthetic Fitness Properties – Fitness extrapolation: it requires that the synthetic fitness provide meaningful inferences beyond the boundaries of the current partial order provided by the user. – Order maintenance : it guarantees that a synthetic fitness is accurate if it maintains the partial ordering given by the user decisions.

33. Surrogate Models Models need to satisfy the above requirements. ε-SVM using a linear kernel

34. ε-SVM Using a linear kernel easily satisfies the fitness extrapolation and order maintenance propertiesImpact of problem visualization. Hyper-plane adjustment Even with a high-regression error, a ε-SVM guarantees the proper ordering of solutions under a tournament selection scheme.

35. Synthesis The surrogate models make a basic assumption: the partial order of user evaluations can be translated into a global numeric value.

36. Synthesis

37. δ(v) as the number of different nodes present on the paths departing from vertex v. φ(v) is defined as the number of different nodes present on the paths arriving to v.

38. Synthesis Estimated ranking may be used to train a ε- SVM.

39. Active Interactive Genetic Algorithms The compact GA is a suitable option to optimize the synthetic fitness Initialization: random individuals, probabilities are initially set to 0.5 Model sampling: generate two candidate solutions by sampling the probability vector. The model sampling procedure is equivalent to uniform crossover in simple GAs.

40. Active Interactive Genetic Algorithms Selection: tournament selection Probabilistic model updating

41. Active Interactive Genetic Algorithms

42. Population-Sizing Model and Convergence- Time Model: approximate form of the cGA is operationally equivalent to the order-one behavior of simple genetic algorithm with steady state selection and uniform crossover.

43. Experimental Results and Analysis Population-Sizing Model: approximate form of the gambler’s ruin population-sizing model Convergence-Time Model: approximate form of Miller and Goldberg’s convergence-time model

44. Experimental Results and Analysis The number of function evaluations required for successful convergence, of GAs as follows:

45. Experimental Results and Analysis IGA requires a population size that grows linearly. It’s the result of using a ε-SVM.

46. Experimental Results and Analysis The active IGA population is also constrained by the three tournament structure. The population size is forced to grow

47. Experimental Results and Analysis The theoretical convergence time of the active iGA with the proper population sizing, should be constant.

48. Experimental Results and Analysis A simple low-cost high-error synthetic fitness function we were able to achieve speedups ranging from 3 up to 7 times.

49. Conclusion of this Paper Propose a heuristic to synthesize a model of the user fitness combining partial ordering concepts, multiobjective optimization ideas, and support vector machines. Model provided by a ε-SVM is able to satisfy the two properties a synthetic fitness need to satisfy—fitness extrapolation, and order maintenance.

50. Conclusion of this Paper The existence of a synthetic fitness allow us to actively use such model to combat user fatigue. The injection of such candidate solutions into the user evaluation process effectively reduce the number of evaluations required on the user side till convergence.

51. Conclusion In application, how to encode needs to be properly designed. The evaluation should be persuasive. The small population size and the user fatigue could just be a trade-off?

52. Ker...